Preserving Identities And Policies Across Replication

ABSTRACT

Preserving identities and policies across replication, including: receiving, at a first storage array, a first data frame comprising data associated a dataset stored at the first storage array and an identifier of a quality of service level associated with the first data frame; generating, at the first storage array, a second data frame comprising a portion of the dataset and the identifier of the quality of service level associated with the first data frame; and transmitting the second data frame to a second storage array.

CROSS-REFERENCE TO RELATED APPLICATIONS

This is a continuation in-part application for patent entitled to afiling date and claiming the benefit of earlier-filed U.S. patentapplication Ser. No. 17/232,668, filed Apr. 16, 2021, hereinincorporated by reference in its entirety, which is a continuation ofU.S. Pat. No. 10,990,306, issued Apr. 27, 2021, which is a continuationof U.S. Pat. No. 10,671,302, issued Jun. 2, 2020.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1A illustrates a first example system for data storage inaccordance with some implementations.

FIG. 1B illustrates a second example system for data storage inaccordance with some implementations.

FIG. 1C illustrates a third example system for data storage inaccordance with some implementations.

FIG. 1D illustrates a fourth example system for data storage inaccordance with some implementations.

FIG. 2A is a perspective view of a storage cluster with multiple storagenodes and internal storage coupled to each storage node to providenetwork attached storage, in accordance with some embodiments.

FIG. 2B is a block diagram showing an interconnect switch couplingmultiple storage nodes in accordance with some embodiments.

FIG. 2C is a multiple level block diagram, showing contents of a storagenode and contents of one of the non-volatile solid state storage unitsin accordance with some embodiments.

FIG. 2D shows a storage server environment, which uses embodiments ofthe storage nodes and storage units of some previous figures inaccordance with some embodiments.

FIG. 2E is a blade hardware block diagram, showing a control plane,compute and storage planes, and authorities interacting with underlyingphysical resources, in accordance with some embodiments.

FIG. 2F depicts elasticity software layers in blades of a storagecluster, in accordance with some embodiments.

FIG. 2G depicts authorities and storage resources in blades of a storagecluster, in accordance with some embodiments.

FIG. 3A sets forth a diagram of a storage system that is coupled fordata communications with a cloud services provider in accordance withsome embodiments of the present disclosure.

FIG. 3B sets forth a diagram of a storage system in accordance with someembodiments of the present disclosure.

FIG. 3C sets forth an example of a cloud-based storage system inaccordance with some embodiments of the present disclosure.

FIG. 3D illustrates an exemplary computing device that may bespecifically configured to perform one or more of the processesdescribed herein.

FIG. 3E illustrates an example of a fleet of storage systems forproviding storage services (also referred to herein as ‘data services’)in accordance with some embodiments.

FIG. 4 sets forth a block diagram illustrating a plurality of storagesystems that support a pod according to some embodiments of the presentdisclosure.

FIG. 5 sets forth a flowchart illustrating an example method of applyinga rate limit across a plurality of storage systems in accordance withsome embodiments of the present disclosure.

FIG. 6 sets forth a flowchart illustrating an additional example methodof applying a rate limit across a plurality of storage systems inaccordance with some embodiments of the present disclosure.

FIG. 7 sets forth a flowchart illustrating an additional example methodof applying a rate limit across a plurality of storage systems inaccordance with some embodiments of the present disclosure.

FIG. 8 sets forth a flowchart illustrating an additional example methodof applying a rate limit across a plurality of storage systems inaccordance with some embodiments of the present disclosure.

FIG. 9 sets forth an example of a data frame header in accordance withsome embodiments of the present disclosure.

FIG. 10 is a block diagram illustrating an example of a storage systemin accordance with some embodiments of the present disclosure.

FIG. 11 is a block diagram illustrating an example of a storage systemin accordance with some embodiments of the present disclosure.

FIG. 12 sets forth a flowchart illustrating an example method ofpreserving identities and policies across replication in accordance withsome embodiments of the present disclosure.

FIG. 13 sets forth a flowchart illustrating an example method ofpreserving identities and policies across replication in accordance withsome embodiments of the present disclosure.

FIG. 14 sets forth a flowchart illustrating an example method ofpreserving identities and policies across replication in accordance withsome embodiments of the present disclosure.

FIG. 15 sets forth a flowchart illustrating an example method ofpreserving identities and policies across replication in accordance withsome embodiments of the present disclosure.

DESCRIPTION OF EMBODIMENTS

Example methods, apparatus, and products for applying a rate limitacross a plurality of storage systems in accordance with embodiments ofthe present disclosure are described with reference to the accompanyingdrawings, beginning with FIG. 1A. FIG. 1A illustrates an example systemfor data storage, in accordance with some implementations. System 100(also referred to as “storage system” herein) includes numerous elementsfor purposes of illustration rather than limitation. It may be notedthat system 100 may include the same, more, or fewer elements configuredin the same or different manner in other implementations.

System 100 includes a number of computing devices 164A-B. Computingdevices (also referred to as “client devices” herein) may be embodied,for example, a server in a data center, a workstation, a personalcomputer, a notebook, or the like. Computing devices 164A-B may becoupled for data communications to one or more storage arrays 102A-Bthrough a storage area network (‘SAN’) 158 or a local area network(‘LAN’) 160.

The SAN 158 may be implemented with a variety of data communicationsfabrics, devices, and protocols. For example, the fabrics for SAN 158may include Fibre Channel, Ethernet, Infiniband, Serial Attached SmallComputer System Interface (‘SAS’), or the like. Data communicationsprotocols for use with SAN 158 may include Advanced TechnologyAttachment (‘ATA’), Fibre Channel Protocol, Small Computer SystemInterface (‘SCSI’), Internet Small Computer System Interface (‘iSCSI’),HyperSCSI, Non-Volatile Memory Express (‘NVMe’) over Fabrics, or thelike. It may be noted that SAN 158 is provided for illustration, ratherthan limitation. Other data communication couplings may be implementedbetween computing devices 164A-B and storage arrays 102A-B.

The LAN 160 may also be implemented with a variety of fabrics, devices,and protocols. For example, the fabrics for LAN 160 may include Ethernet(802.3), wireless (802.11), or the like. Data communication protocolsfor use in LAN 160 may include Transmission Control Protocol (‘TCP’),User Datagram Protocol (‘UDP’), Internet Protocol (‘IP’), HyperTextTransfer Protocol (‘HTTP’), Wireless Access Protocol (‘WAP’), HandheldDevice Transport Protocol (‘HDTP’), Session Initiation Protocol (‘SIP’),Real Time Protocol (‘RTP’), or the like.

Storage arrays 102A-B may provide persistent data storage for thecomputing devices 164A-B. Storage array 102A may be contained in achassis (not shown), and storage array 102B may be contained in anotherchassis (not shown), in implementations. Storage array 102A and 102B mayinclude one or more storage array controllers 110A-D (also referred toas “controller” herein). A storage array controller 110A-D may beembodied as a module of automated computing machinery comprisingcomputer hardware, computer software, or a combination of computerhardware and software. In some implementations, the storage arraycontrollers 110A-D may be configured to carry out various storage tasks.Storage tasks may include writing data received from the computingdevices 164A-B to storage array 102A-B, erasing data from storage array102A-B, retrieving data from storage array 102A-B and providing data tocomputing devices 164A-B, monitoring and reporting of disk utilizationand performance, performing redundancy operations, such as RedundantArray of Independent Drives (‘RAID’) or RAID-like data redundancyoperations, compressing data, encrypting data, and so forth.

Storage array controller 110A-D may be implemented in a variety of ways,including as a Field Programmable Gate Array (‘FPGA’), a ProgrammableLogic Chip (‘PLC’), an Application Specific Integrated Circuit (‘ASIC’),System-on-Chip (‘SOC’), or any computing device that includes discretecomponents such as a processing device, central processing unit,computer memory, or various adapters. Storage array controller 110A-Dmay include, for example, a data communications adapter configured tosupport communications via the SAN 158 or LAN 160. In someimplementations, storage array controller 110A-D may be independentlycoupled to the LAN 160. In implementations, storage array controller110A-D may include an I/O controller or the like that couples thestorage array controller 110A-D for data communications, through amidplane (not shown), to a persistent storage resource 170A-B (alsoreferred to as a “storage resource” herein). The persistent storageresource 170A-B main include any number of storage drives 171A-F (alsoreferred to as “storage devices” herein) and any number of non-volatileRandom Access Memory (‘NVRAM’) devices (not shown).

In some implementations, the NVRAM devices of a persistent storageresource 170A-B may be configured to receive, from the storage arraycontroller 110A-D, data to be stored in the storage drives 171A-F. Insome examples, the data may originate from computing devices 164A-B. Insome examples, writing data to the NVRAM device may be carried out morequickly than directly writing data to the storage drive 171A-F. Inimplementations, the storage array controller 110A-D may be configuredto utilize the NVRAM devices as a quickly accessible buffer for datadestined to be written to the storage drives 171A-F. Latency for writerequests using NVRAM devices as a buffer may be improved relative to asystem in which a storage array controller 110A-D writes data directlyto the storage drives 171A-F. In some implementations, the NVRAM devicesmay be implemented with computer memory in the form of high bandwidth,low latency RAM. The NVRAM device is referred to as “non-volatile”because the NVRAM device may receive or include a unique power sourcethat maintains the state of the RAM after main power loss to the NVRAMdevice. Such a power source may be a battery, one or more capacitors, orthe like. In response to a power loss, the NVRAM device may beconfigured to write the contents of the RAM to a persistent storage,such as the storage drives 171A-F.

In implementations, storage drive 171A-F may refer to any deviceconfigured to record data persistently, where “persistently” or“persistent” refers as to a device's ability to maintain recorded dataafter loss of power. In some implementations, storage drive 171A-F maycorrespond to non-disk storage media. For example, the storage drive171A-F may be one or more solid-state drives (‘SSDs’), flash memorybased storage, any type of solid-state non-volatile memory, or any othertype of non-mechanical storage device. In other implementations, storagedrive 171A-F may include mechanical or spinning hard disk, such ashard-disk drives (‘HDD’).

In some implementations, the storage array controllers 110A-D may beconfigured for offloading device management responsibilities fromstorage drive 171A-F in storage array 102A-B. For example, storage arraycontrollers 110A-D may manage control information that may describe thestate of one or more memory blocks in the storage drives 171A-F. Thecontrol information may indicate, for example, that a particular memoryblock has failed and should no longer be written to, that a particularmemory block contains boot code for a storage array controller 110A-D,the number of program-erase (‘P/E’) cycles that have been performed on aparticular memory block, the age of data stored in a particular memoryblock, the type of data that is stored in a particular memory block, andso forth. In some implementations, the control information may be storedwith an associated memory block as metadata. In other implementations,the control information for the storage drives 171A-F may be stored inone or more particular memory blocks of the storage drives 171A-F thatare selected by the storage array controller 110A-D. The selected memoryblocks may be tagged with an identifier indicating that the selectedmemory block contains control information. The identifier may beutilized by the storage array controllers 110A-D in conjunction withstorage drives 171A-F to quickly identify the memory blocks that containcontrol information. For example, the storage controllers 110A-D mayissue a command to locate memory blocks that contain controlinformation. It may be noted that control information may be so largethat parts of the control information may be stored in multiplelocations, that the control information may be stored in multiplelocations for purposes of redundancy, for example, or that the controlinformation may otherwise be distributed across multiple memory blocksin the storage drive 171A-F.

In implementations, storage array controllers 110A-D may offload devicemanagement responsibilities from storage drives 171A-F of storage array102A-B by retrieving, from the storage drives 171A-F, controlinformation describing the state of one or more memory blocks in thestorage drives 171A-F. Retrieving the control information from thestorage drives 171A-F may be carried out, for example, by the storagearray controller 110A-D querying the storage drives 171A-F for thelocation of control information for a particular storage drive 171A-F.The storage drives 171A-F may be configured to execute instructions thatenable the storage drive 171A-F to identify the location of the controlinformation. The instructions may be executed by a controller (notshown) associated with or otherwise located on the storage drive 171A-Fand may cause the storage drive 171A-F to scan a portion of each memoryblock to identify the memory blocks that store control information forthe storage drives 171A-F. The storage drives 171A-F may respond bysending a response message to the storage array controller 110A-D thatincludes the location of control information for the storage drive171A-F. Responsive to receiving the response message, storage arraycontrollers 110A-D may issue a request to read data stored at theaddress associated with the location of control information for thestorage drives 171A-F.

In other implementations, the storage array controllers 110A-D mayfurther offload device management responsibilities from storage drives171A-F by performing, in response to receiving the control information,a storage drive management operation. A storage drive managementoperation may include, for example, an operation that is typicallyperformed by the storage drive 171A-F (e.g., the controller (not shown)associated with a particular storage drive 171A-F). A storage drivemanagement operation may include, for example, ensuring that data is notwritten to failed memory blocks within the storage drive 171A-F,ensuring that data is written to memory blocks within the storage drive171A-F in such a way that adequate wear leveling is achieved, and soforth.

In implementations, storage array 102A-B may implement two or morestorage array controllers 110A-D. For example, storage array 102A mayinclude storage array controllers 110A and storage array controllers110B. At a given instance, a single storage array controller 110A-D(e.g., storage array controller 110A) of a storage system 100 may bedesignated with primary status (also referred to as “primary controller”herein), and other storage array controllers 110A-D (e.g., storage arraycontroller 110A) may be designated with secondary status (also referredto as “secondary controller” herein). The primary controller may haveparticular rights, such as permission to alter data in persistentstorage resource 170A-B (e.g., writing data to persistent storageresource 170A-B). At least some of the rights of the primary controllermay supersede the rights of the secondary controller. For instance, thesecondary controller may not have permission to alter data in persistentstorage resource 170A-B when the primary controller has the right. Thestatus of storage array controllers 110A-D may change. For example,storage array controller 110A may be designated with secondary status,and storage array controller 110B may be designated with primary status.

In some implementations, a primary controller, such as storage arraycontroller 110A, may serve as the primary controller for one or morestorage arrays 102A-B, and a second controller, such as storage arraycontroller 110B, may serve as the secondary controller for the one ormore storage arrays 102A-B. For example, storage array controller 110Amay be the primary controller for storage array 102A and storage array102B, and storage array controller 110B may be the secondary controllerfor storage array 102A and 102B. In some implementations, storage arraycontrollers 110C and 110D (also referred to as “storage processingmodules”) may neither have primary or secondary status. Storage arraycontrollers 110C and 110D, implemented as storage processing modules,may act as a communication interface between the primary and secondarycontrollers (e.g., storage array controllers 110A and 110B,respectively) and storage array 102B. For example, storage arraycontroller 110A of storage array 102A may send a write request, via SAN158, to storage array 102B. The write request may be received by bothstorage array controllers 110C and 110D of storage array 102B. Storagearray controllers 110C and 110D facilitate the communication, e.g., sendthe write request to the appropriate storage drive 171A-F. It may benoted that in some implementations storage processing modules may beused to increase the number of storage drives controlled by the primaryand secondary controllers.

In implementations, storage array controllers 110A-D are communicativelycoupled, via a midplane (not shown), to one or more storage drives171A-F and to one or more NVRAM devices (not shown) that are included aspart of a storage array 102A-B. The storage array controllers 110A-D maybe coupled to the midplane via one or more data communication links andthe midplane may be coupled to the storage drives 171A-F and the NVRAMdevices via one or more data communications links. The datacommunications links described herein are collectively illustrated bydata communications links 108A-D and may include a Peripheral ComponentInterconnect Express (‘PCIe’) bus, for example.

FIG. 1B illustrates an example system for data storage, in accordancewith some implementations. Storage array controller 101 illustrated inFIG. 1B may be similar to the storage array controllers 110A-D describedwith respect to FIG. 1A. In one example, storage array controller 101may be similar to storage array controller 110A or storage arraycontroller 110B. Storage array controller 101 includes numerous elementsfor purposes of illustration rather than limitation. It may be notedthat storage array controller 101 may include the same, more, or fewerelements configured in the same or different manner in otherimplementations. It may be noted that elements of FIG. 1A may beincluded below to help illustrate features of storage array controller101.

Storage array controller 101 may include one or more processing devices104 and random access memory (‘RAM’) 111. Processing device 104 (orcontroller 101) represents one or more general-purpose processingdevices such as a microprocessor, central processing unit, or the like.More particularly, the processing device 104 (or controller 101) may bea complex instruction set computing (‘CISC’) microprocessor, reducedinstruction set computing (‘RISC’) microprocessor, very long instructionword (‘VLIW’) microprocessor, or a processor implementing otherinstruction sets or processors implementing a combination of instructionsets. The processing device 104 (or controller 101) may also be one ormore special-purpose processing devices such as an ASIC, an FPGA, adigital signal processor (‘DSP’), network processor, or the like.

The processing device 104 may be connected to the RAM 111 via a datacommunications link 106, which may be embodied as a high speed memorybus such as a Double-Data Rate 4 (‘DDR4’) bus. Stored in RAM 111 is anoperating system 112. In some implementations, instructions 113 arestored in RAM 111. Instructions 113 may include computer programinstructions for performing operations in in a direct-mapped flashstorage system. In one embodiment, a direct-mapped flash storage systemis one that that addresses data blocks within flash drives directly andwithout an address translation performed by the storage controllers ofthe flash drives.

In implementations, storage array controller 101 includes one or morehost bus adapters 103A-C that are coupled to the processing device 104via a data communications link 105A-C. In implementations, host busadapters 103A-C may be computer hardware that connects a host system(e.g., the storage array controller) to other network and storagearrays. In some examples, host bus adapters 103A-C may be a FibreChannel adapter that enables the storage array controller 101 to connectto a SAN, an Ethernet adapter that enables the storage array controller101 to connect to a LAN, or the like. Host bus adapters 103A-C may becoupled to the processing device 104 via a data communications link105A-C such as, for example, a PCIe bus.

In implementations, storage array controller 101 may include a host busadapter 114 that is coupled to an expander 115. The expander 115 may beused to attach a host system to a larger number of storage drives. Theexpander 115 may, for example, be a SAS expander utilized to enable thehost bus adapter 114 to attach to storage drives in an implementationwhere the host bus adapter 114 is embodied as a SAS controller.

In implementations, storage array controller 101 may include a switch116 coupled to the processing device 104 via a data communications link109. The switch 116 may be a computer hardware device that can createmultiple endpoints out of a single endpoint, thereby enabling multipledevices to share a single endpoint. The switch 116 may, for example, bea PCIe switch that is coupled to a PCIe bus (e.g., data communicationslink 109) and presents multiple PCIe connection points to the midplane.

In implementations, storage array controller 101 includes a datacommunications link 107 for coupling the storage array controller 101 toother storage array controllers. In some examples, data communicationslink 107 may be a QuickPath Interconnect (QPI) interconnect.

A traditional storage system that uses traditional flash drives mayimplement a process across the flash drives that are part of thetraditional storage system. For example, a higher level process of thestorage system may initiate and control a process across the flashdrives. However, a flash drive of the traditional storage system mayinclude its own storage controller that also performs the process. Thus,for the traditional storage system, a higher level process (e.g.,initiated by the storage system) and a lower level process (e.g.,initiated by a storage controller of the storage system) may both beperformed.

To resolve various deficiencies of a traditional storage system,operations may be performed by higher level processes and not by thelower level processes. For example, the flash storage system may includeflash drives that do not include storage controllers that provide theprocess. Thus, the operating system of the flash storage system itselfmay initiate and control the process. This may be accomplished by adirect-mapped flash storage system that addresses data blocks within theflash drives directly and without an address translation performed bythe storage controllers of the flash drives.

In implementations, storage drive 171A-F may be one or more zonedstorage devices. In some implementations, the one or more zoned storagedevices may be a shingled HDD. In implementations, the one or morestorage devices may be a flash-based SSD. In a zoned storage device, azoned namespace on the zoned storage device can be addressed by groupsof blocks that are grouped and aligned by a natural size, forming anumber of addressable zones. In implementations utilizing an SSD, thenatural size may be based on the erase block size of the SSD. In someimplementations, the zones of the zoned storage device may be definedduring initialization of the zoned storage device. In implementations,the zones may be defined dynamically as data is written to the zonedstorage device.

In some implementations, zones may be heterogeneous, with some zoneseach being a page group and other zones being multiple page groups. Inimplementations, some zones may correspond to an erase block and otherzones may correspond to multiple erase blocks. In an implementation,zones may be any combination of differing numbers of pages in pagegroups and/or erase blocks, for heterogeneous mixes of programmingmodes, manufacturers, product types and/or product generations ofstorage devices, as applied to heterogeneous assemblies, upgrades,distributed storages, etc. In some implementations, zones may be definedas having usage characteristics, such as a property of supporting datawith particular kinds of longevity (very short lived or very long lived,for example). These properties could be used by a zoned storage deviceto determine how the zone will be managed over the zone's expectedlifetime.

It should be appreciated that a zone is a virtual construct. Anyparticular zone may not have a fixed location at a storage device. Untilallocated, a zone may not have any location at a storage device. A zonemay correspond to a number representing a chunk of virtually allocatablespace that is the size of an erase block or other block size in variousimplementations. When the system allocates or opens a zone, zones getallocated to flash or other solid-state storage memory and, as thesystem writes to the zone, pages are written to that mapped flash orother solid-state storage memory of the zoned storage device. When thesystem closes the zone, the associated erase block(s) or other sizedblock(s) are completed. At some point in the future, the system maydelete a zone which will free up the zone's allocated space. During itslifetime, a zone may be moved around to different locations of the zonedstorage device, e.g., as the zoned storage device does internalmaintenance.

In implementations, the zones of the zoned storage device may be indifferent states. A zone may be in an empty state in which data has notbeen stored at the zone. An empty zone may be opened explicitly, orimplicitly by writing data to the zone. This is the initial state forzones on a fresh zoned storage device, but may also be the result of azone reset. In some implementations, an empty zone may have a designatedlocation within the flash memory of the zoned storage device. In animplementation, the location of the empty zone may be chosen when thezone is first opened or first written to (or later if writes arebuffered into memory). A zone may be in an open state either implicitlyor explicitly, where a zone that is in an open state may be written tostore data with write or append commands. In an implementation, a zonethat is in an open state may also be written to using a copy commandthat copies data from a different zone. In some implementations, a zonedstorage device may have a limit on the number of open zones at aparticular time.

A zone in a closed state is a zone that has been partially written to,but has entered a closed state after issuing an explicit closeoperation. A zone in a closed state may be left available for futurewrites, but may reduce some of the run-time overhead consumed by keepingthe zone in an open state. In implementations, a zoned storage devicemay have a limit on the number of closed zones at a particular time. Azone in a full state is a zone that is storing data and can no longer bewritten to. A zone may be in a full state either after writes havewritten data to the entirety of the zone or as a result of a zone finishoperation. Prior to a finish operation, a zone may or may not have beencompletely written. After a finish operation, however, the zone may notbe opened a written to further without first performing a zone resetoperation.

The mapping from a zone to an erase block (or to a shingled track in anHDD) may be arbitrary, dynamic, and hidden from view. The process ofopening a zone may be an operation that allows a new zone to bedynamically mapped to underlying storage of the zoned storage device,and then allows data to be written through appending writes into thezone until the zone reaches capacity. The zone can be finished at anypoint, after which further data may not be written into the zone. Whenthe data stored at the zone is no longer needed, the zone can be resetwhich effectively deletes the zone's content from the zoned storagedevice, making the physical storage held by that zone available for thesubsequent storage of data. Once a zone has been written and finished,the zoned storage device ensures that the data stored at the zone is notlost until the zone is reset. In the time between writing the data tothe zone and the resetting of the zone, the zone may be moved aroundbetween shingle tracks or erase blocks as part of maintenance operationswithin the zoned storage device, such as by copying data to keep thedata refreshed or to handle memory cell aging in an SSD.

In implementations utilizing an HDD, the resetting of the zone may allowthe shingle tracks to be allocated to a new, opened zone that may beopened at some point in the future. In implementations utilizing an SSD,the resetting of the zone may cause the associated physical eraseblock(s) of the zone to be erased and subsequently reused for thestorage of data. In some implementations, the zoned storage device mayhave a limit on the number of open zones at a point in time to reducethe amount of overhead dedicated to keeping zones open.

The operating system of the flash storage system may identify andmaintain a list of allocation units across multiple flash drives of theflash storage system. The allocation units may be entire erase blocks ormultiple erase blocks. The operating system may maintain a map oraddress range that directly maps addresses to erase blocks of the flashdrives of the flash storage system.

Direct mapping to the erase blocks of the flash drives may be used torewrite data and erase data. For example, the operations may beperformed on one or more allocation units that include a first data anda second data where the first data is to be retained and the second datais no longer being used by the flash storage system. The operatingsystem may initiate the process to write the first data to new locationswithin other allocation units and erasing the second data and markingthe allocation units as being available for use for subsequent data.Thus, the process may only be performed by the higher level operatingsystem of the flash storage system without an additional lower levelprocess being performed by controllers of the flash drives.

Advantages of the process being performed only by the operating systemof the flash storage system include increased reliability of the flashdrives of the flash storage system as unnecessary or redundant writeoperations are not being performed during the process. One possiblepoint of novelty here is the concept of initiating and controlling theprocess at the operating system of the flash storage system. Inaddition, the process can be controlled by the operating system acrossmultiple flash drives. This is contrast to the process being performedby a storage controller of a flash drive.

A storage system can consist of two storage array controllers that sharea set of drives for failover purposes, or it could consist of a singlestorage array controller that provides a storage service that utilizesmultiple drives, or it could consist of a distributed network of storagearray controllers each with some number of drives or some amount ofFlash storage where the storage array controllers in the networkcollaborate to provide a complete storage service and collaborate onvarious aspects of a storage service including storage allocation andgarbage collection.

FIG. 1C illustrates a third example system 117 for data storage inaccordance with some implementations. System 117 (also referred to as“storage system” herein) includes numerous elements for purposes ofillustration rather than limitation. It may be noted that system 117 mayinclude the same, more, or fewer elements configured in the same ordifferent manner in other implementations.

In one embodiment, system 117 includes a dual Peripheral ComponentInterconnect (‘PCI’) flash storage device 118 with separatelyaddressable fast write storage. System 117 may include a storage devicecontroller 119. In one embodiment, storage device controller 119A-D maybe a CPU, ASIC, FPGA, or any other circuitry that may implement controlstructures necessary according to the present disclosure. In oneembodiment, system 117 includes flash memory devices (e.g., includingflash memory devices 120 a-n), operatively coupled to various channelsof the storage device controller 119. Flash memory devices 120 a-n, maybe presented to the controller 119A-D as an addressable collection ofFlash pages, erase blocks, and/or control elements sufficient to allowthe storage device controller 119A-D to program and retrieve variousaspects of the Flash. In one embodiment, storage device controller119A-D may perform operations on flash memory devices 120 a-n includingstoring and retrieving data content of pages, arranging and erasing anyblocks, tracking statistics related to the use and reuse of Flash memorypages, erase blocks, and cells, tracking and predicting error codes andfaults within the Flash memory, controlling voltage levels associatedwith programming and retrieving contents of Flash cells, etc.

In one embodiment, system 117 may include RAM 121 to store separatelyaddressable fast-write data. In one embodiment, RAM 121 may be one ormore separate discrete devices. In another embodiment, RAM 121 may beintegrated into storage device controller 119A-D or multiple storagedevice controllers. The RAM 121 may be utilized for other purposes aswell, such as temporary program memory for a processing device (e.g., aCPU) in the storage device controller 119.

In one embodiment, system 117 may include a stored energy device 122,such as a rechargeable battery or a capacitor. Stored energy device 122may store energy sufficient to power the storage device controller 119,some amount of the RAM (e.g., RAM 121), and some amount of Flash memory(e.g., Flash memory 120 a-120 n) for sufficient time to write thecontents of RAM to Flash memory. In one embodiment, storage devicecontroller 119A-D may write the contents of RAM to Flash Memory if thestorage device controller detects loss of external power.

In one embodiment, system 117 includes two data communications links 123a, 123 b. In one embodiment, data communications links 123 a, 123 b maybe PCI interfaces. In another embodiment, data communications links 123a, 123 b may be based on other communications standards (e.g.,HyperTransport, InfiniBand, etc.). Data communications links 123 a, 123b may be based on non-volatile memory express (‘NVMe’) or NVMe overfabrics (‘NVMf’) specifications that allow external connection to thestorage device controller 119A-D from other components in the storagesystem 117. It should be noted that data communications links may beinterchangeably referred to herein as PCI buses for convenience.

System 117 may also include an external power source (not shown), whichmay be provided over one or both data communications links 123 a, 123 b,or which may be provided separately. An alternative embodiment includesa separate Flash memory (not shown) dedicated for use in storing thecontent of RAM 121. The storage device controller 119A-D may present alogical device over a PCI bus which may include an addressablefast-write logical device, or a distinct part of the logical addressspace of the storage device 118, which may be presented as PCI memory oras persistent storage. In one embodiment, operations to store into thedevice are directed into the RAM 121. On power failure, the storagedevice controller 119A-D may write stored content associated with theaddressable fast-write logical storage to Flash memory (e.g., Flashmemory 120 a-n) for long-term persistent storage.

In one embodiment, the logical device may include some presentation ofsome or all of the content of the Flash memory devices 120 a-n, wherethat presentation allows a storage system including a storage device 118(e.g., storage system 117) to directly address Flash memory pages anddirectly reprogram erase blocks from storage system components that areexternal to the storage device through the PCI bus. The presentation mayalso allow one or more of the external components to control andretrieve other aspects of the Flash memory including some or all of:tracking statistics related to use and reuse of Flash memory pages,erase blocks, and cells across all the Flash memory devices; trackingand predicting error codes and faults within and across the Flash memorydevices; controlling voltage levels associated with programming andretrieving contents of Flash cells; etc.

In one embodiment, the stored energy device 122 may be sufficient toensure completion of in-progress operations to the Flash memory devices120 a-120 n stored energy device 122 may power storage device controller119A-D and associated Flash memory devices (e.g., 120 a-n) for thoseoperations, as well as for the storing of fast-write RAM to Flashmemory. Stored energy device 122 may be used to store accumulatedstatistics and other parameters kept and tracked by the Flash memorydevices 120 a-n and/or the storage device controller 119. Separatecapacitors or stored energy devices (such as smaller capacitors near orembedded within the Flash memory devices themselves) may be used forsome or all of the operations described herein.

Various schemes may be used to track and optimize the life span of thestored energy component, such as adjusting voltage levels over time,partially discharging the stored energy device 122 to measurecorresponding discharge characteristics, etc. If the available energydecreases over time, the effective available capacity of the addressablefast-write storage may be decreased to ensure that it can be writtensafely based on the currently available stored energy.

FIG. 1D illustrates a third example storage system 124 for data storagein accordance with some implementations. In one embodiment, storagesystem 124 includes storage controllers 125 a, 125 b. In one embodiment,storage controllers 125 a, 125 b are operatively coupled to Dual PCIstorage devices. Storage controllers 125 a, 125 b may be operativelycoupled (e.g., via a storage network 130) to some number of hostcomputers 127 a-n.

In one embodiment, two storage controllers (e.g., 125 a and 125 b)provide storage services, such as a SCS) block storage array, a fileserver, an object server, a database or data analytics service, etc. Thestorage controllers 125 a, 125 b may provide services through somenumber of network interfaces (e.g., 126 a-d) to host computers 127 a-noutside of the storage system 124. Storage controllers 125 a, 125 b mayprovide integrated services or an application entirely within thestorage system 124, forming a converged storage and compute system. Thestorage controllers 125 a, 125 b may utilize the fast write memorywithin or across storage devices 119 a-d to journal in progressoperations to ensure the operations are not lost on a power failure,storage controller removal, storage controller or storage systemshutdown, or some fault of one or more software or hardware componentswithin the storage system 124.

In one embodiment, storage controllers 125 a, 125 b operate as PCImasters to one or the other PCI buses 128 a, 128 b. In anotherembodiment, 128 a and 128 b may be based on other communicationsstandards (e.g., HyperTransport, InfiniBand, etc.). Other storage systemembodiments may operate storage controllers 125 a, 125 b asmulti-masters for both PCI buses 128 a, 128 b. Alternately, aPCI/NVMe/NVMf switching infrastructure or fabric may connect multiplestorage controllers. Some storage system embodiments may allow storagedevices to communicate with each other directly rather thancommunicating only with storage controllers. In one embodiment, astorage device controller 119 a may be operable under direction from astorage controller 125 a to synthesize and transfer data to be storedinto Flash memory devices from data that has been stored in RAM (e.g.,RAM 121 of FIG. 1C). For example, a recalculated version of RAM contentmay be transferred after a storage controller has determined that anoperation has fully committed across the storage system, or whenfast-write memory on the device has reached a certain used capacity, orafter a certain amount of time, to ensure improve safety of the data orto release addressable fast-write capacity for reuse. This mechanism maybe used, for example, to avoid a second transfer over a bus (e.g., 128a, 128 b) from the storage controllers 125 a, 125 b. In one embodiment,a recalculation may include compressing data, attaching indexing orother metadata, combining multiple data segments together, performingerasure code calculations, etc.

In one embodiment, under direction from a storage controller 125 a, 125b, a storage device controller 119 a, 119 b may be operable to calculateand transfer data to other storage devices from data stored in RAM(e.g., RAM 121 of FIG. 1C) without involvement of the storagecontrollers 125 a, 125 b. This operation may be used to mirror datastored in one storage controller 125 a to another storage controller 125b, or it could be used to offload compression, data aggregation, and/orerasure coding calculations and transfers to storage devices to reduceload on storage controllers or the storage controller interface 129 a,129 b to the PCI bus 128 a, 128 b.

A storage device controller 119A-D may include mechanisms forimplementing high availability primitives for use by other parts of astorage system external to the Dual PCI storage device 118. For example,reservation or exclusion primitives may be provided so that, in astorage system with two storage controllers providing a highly availablestorage service, one storage controller may prevent the other storagecontroller from accessing or continuing to access the storage device.This could be used, for example, in cases where one controller detectsthat the other controller is not functioning properly or where theinterconnect between the two storage controllers may itself not befunctioning properly.

In one embodiment, a storage system for use with Dual PCI direct mappedstorage devices with separately addressable fast write storage includessystems that manage erase blocks or groups of erase blocks as allocationunits for storing data on behalf of the storage service, or for storingmetadata (e.g., indexes, logs, etc.) associated with the storageservice, or for proper management of the storage system itself. Flashpages, which may be a few kilobytes in size, may be written as dataarrives or as the storage system is to persist data for long intervalsof time (e.g., above a defined threshold of time). To commit data morequickly, or to reduce the number of writes to the Flash memory devices,the storage controllers may first write data into the separatelyaddressable fast write storage on one more storage devices.

In one embodiment, the storage controllers 125 a, 125 b may initiate theuse of erase blocks within and across storage devices (e.g., 118) inaccordance with an age and expected remaining lifespan of the storagedevices, or based on other statistics. The storage controllers 125 a,125 b may initiate garbage collection and data migration data betweenstorage devices in accordance with pages that are no longer needed aswell as to manage Flash page and erase block lifespans and to manageoverall system performance.

In one embodiment, the storage system 124 may utilize mirroring and/orerasure coding schemes as part of storing data into addressable fastwrite storage and/or as part of writing data into allocation unitsassociated with erase blocks. Erasure codes may be used across storagedevices, as well as within erase blocks or allocation units, or withinand across Flash memory devices on a single storage device, to provideredundancy against single or multiple storage device failures or toprotect against internal corruptions of Flash memory pages resultingfrom Flash memory operations or from degradation of Flash memory cells.Mirroring and erasure coding at various levels may be used to recoverfrom multiple types of failures that occur separately or in combination.

The embodiments depicted with reference to FIGS. 2A-G illustrate astorage cluster that stores user data, such as user data originatingfrom one or more user or client systems or other sources external to thestorage cluster. The storage cluster distributes user data acrossstorage nodes housed within a chassis, or across multiple chassis, usingerasure coding and redundant copies of metadata. Erasure coding refersto a method of data protection or reconstruction in which data is storedacross a set of different locations, such as disks, storage nodes orgeographic locations. Flash memory is one type of solid-state memorythat may be integrated with the embodiments, although the embodimentsmay be extended to other types of solid-state memory or other storagemedium, including non-solid state memory. Control of storage locationsand workloads are distributed across the storage locations in aclustered peer-to-peer system. Tasks such as mediating communicationsbetween the various storage nodes, detecting when a storage node hasbecome unavailable, and balancing I/Os (inputs and outputs) across thevarious storage nodes, are all handled on a distributed basis. Data islaid out or distributed across multiple storage nodes in data fragmentsor stripes that support data recovery in some embodiments. Ownership ofdata can be reassigned within a cluster, independent of input and outputpatterns. This architecture described in more detail below allows astorage node in the cluster to fail, with the system remainingoperational, since the data can be reconstructed from other storagenodes and thus remain available for input and output operations. Invarious embodiments, a storage node may be referred to as a clusternode, a blade, or a server.

The storage cluster may be contained within a chassis, i.e., anenclosure housing one or more storage nodes. A mechanism to providepower to each storage node, such as a power distribution bus, and acommunication mechanism, such as a communication bus that enablescommunication between the storage nodes are included within the chassis.The storage cluster can run as an independent system in one locationaccording to some embodiments. In one embodiment, a chassis contains atleast two instances of both the power distribution and the communicationbus which may be enabled or disabled independently. The internalcommunication bus may be an Ethernet bus, however, other technologiessuch as PCIe, InfiniBand, and others, are equally suitable. The chassisprovides a port for an external communication bus for enablingcommunication between multiple chassis, directly or through a switch,and with client systems. The external communication may use a technologysuch as Ethernet, InfiniBand, Fibre Channel, etc. In some embodiments,the external communication bus uses different communication bustechnologies for inter-chassis and client communication. If a switch isdeployed within or between chassis, the switch may act as a translationbetween multiple protocols or technologies. When multiple chassis areconnected to define a storage cluster, the storage cluster may beaccessed by a client using either proprietary interfaces or standardinterfaces such as network file system (‘NFS’), common internet filesystem (‘CIFS’), small computer system interface (‘SCSI’) or hypertexttransfer protocol (‘HTTP’). Translation from the client protocol mayoccur at the switch, chassis external communication bus or within eachstorage node. In some embodiments, multiple chassis may be coupled orconnected to each other through an aggregator switch. A portion and/orall of the coupled or connected chassis may be designated as a storagecluster. As discussed above, each chassis can have multiple blades, eachblade has a media access control (‘MAC’) address, but the storagecluster is presented to an external network as having a single clusterIP address and a single MAC address in some embodiments.

Each storage node may be one or more storage servers and each storageserver is connected to one or more non-volatile solid state memoryunits, which may be referred to as storage units or storage devices. Oneembodiment includes a single storage server in each storage node andbetween one to eight non-volatile solid state memory units, however thisone example is not meant to be limiting. The storage server may includea processor, DRAM and interfaces for the internal communication bus andpower distribution for each of the power buses. Inside the storage node,the interfaces and storage unit share a communication bus, e.g., PCIExpress, in some embodiments. The non-volatile solid state memory unitsmay directly access the internal communication bus interface through astorage node communication bus, or request the storage node to accessthe bus interface. The non-volatile solid state memory unit contains anembedded CPU, solid state storage controller, and a quantity of solidstate mass storage, e.g., between 2-32 terabytes (‘TB’) in someembodiments. An embedded volatile storage medium, such as DRAM, and anenergy reserve apparatus are included in the non-volatile solid statememory unit. In some embodiments, the energy reserve apparatus is acapacitor, super-capacitor, or battery that enables transferring asubset of DRAM contents to a stable storage medium in the case of powerloss. In some embodiments, the non-volatile solid state memory unit isconstructed with a storage class memory, such as phase change ormagnetoresistive random access memory (‘MRAM’) that substitutes for DRAMand enables a reduced power hold-up apparatus.

One of many features of the storage nodes and non-volatile solid statestorage is the ability to proactively rebuild data in a storage cluster.The storage nodes and non-volatile solid state storage can determinewhen a storage node or non-volatile solid state storage in the storagecluster is unreachable, independent of whether there is an attempt toread data involving that storage node or non-volatile solid statestorage. The storage nodes and non-volatile solid state storage thencooperate to recover and rebuild the data in at least partially newlocations. This constitutes a proactive rebuild, in that the systemrebuilds data without waiting until the data is needed for a read accessinitiated from a client system employing the storage cluster. These andfurther details of the storage memory and operation thereof arediscussed below.

FIG. 2A is a perspective view of a storage cluster 161, with multiplestorage nodes 150 and internal solid-state memory coupled to eachstorage node to provide network attached storage or storage areanetwork, in accordance with some embodiments. A network attachedstorage, storage area network, or a storage cluster, or other storagememory, could include one or more storage clusters 161, each having oneor more storage nodes 150, in a flexible and reconfigurable arrangementof both the physical components and the amount of storage memoryprovided thereby. The storage cluster 161 is designed to fit in a rack,and one or more racks can be set up and populated as desired for thestorage memory. The storage cluster 161 has a chassis 138 havingmultiple slots 142. It should be appreciated that chassis 138 may bereferred to as a housing, enclosure, or rack unit. In one embodiment,the chassis 138 has fourteen slots 142, although other numbers of slotsare readily devised. For example, some embodiments have four slots,eight slots, sixteen slots, thirty-two slots, or other suitable numberof slots. Each slot 142 can accommodate one storage node 150 in someembodiments. Chassis 138 includes flaps 148 that can be utilized tomount the chassis 138 on a rack. Fans 144 provide air circulation forcooling of the storage nodes 150 and components thereof, although othercooling components could be used, or an embodiment could be devisedwithout cooling components. A switch fabric 146 couples storage nodes150 within chassis 138 together and to a network for communication tothe memory. In an embodiment depicted in herein, the slots 142 to theleft of the switch fabric 146 and fans 144 are shown occupied by storagenodes 150, while the slots 142 to the right of the switch fabric 146 andfans 144 are empty and available for insertion of storage node 150 forillustrative purposes. This configuration is one example, and one ormore storage nodes 150 could occupy the slots 142 in various furtherarrangements. The storage node arrangements need not be sequential oradjacent in some embodiments. Storage nodes 150 are hot pluggable,meaning that a storage node 150 can be inserted into a slot 142 in thechassis 138, or removed from a slot 142, without stopping or poweringdown the system. Upon insertion or removal of storage node 150 from slot142, the system automatically reconfigures in order to recognize andadapt to the change. Reconfiguration, in some embodiments, includesrestoring redundancy and/or rebalancing data or load.

Each storage node 150 can have multiple components. In the embodimentshown here, the storage node 150 includes a printed circuit board 159populated by a CPU 156, i.e., processor, a memory 154 coupled to the CPU156, and a non-volatile solid state storage 152 coupled to the CPU 156,although other mountings and/or components could be used in furtherembodiments. The memory 154 has instructions which are executed by theCPU 156 and/or data operated on by the CPU 156. As further explainedbelow, the non-volatile solid state storage 152 includes flash or, infurther embodiments, other types of solid-state memory.

Referring to FIG. 2A, storage cluster 161 is scalable, meaning thatstorage capacity with non-uniform storage sizes is readily added, asdescribed above. One or more storage nodes 150 can be plugged into orremoved from each chassis and the storage cluster self-configures insome embodiments. Plug-in storage nodes 150, whether installed in achassis as delivered or later added, can have different sizes. Forexample, in one embodiment a storage node 150 can have any multiple of 4TB, e.g., 8 TB, 12 TB, 16 TB, 32 TB, etc. In further embodiments, astorage node 150 could have any multiple of other storage amounts orcapacities. Storage capacity of each storage node 150 is broadcast, andinfluences decisions of how to stripe the data. For maximum storageefficiency, an embodiment can self-configure as wide as possible in thestripe, subject to a predetermined requirement of continued operationwith loss of up to one, or up to two, non-volatile solid state storage152 units or storage nodes 150 within the chassis.

FIG. 2B is a block diagram showing a communications interconnect 173 andpower distribution bus 172 coupling multiple storage nodes 150.Referring back to FIG. 2A, the communications interconnect 173 can beincluded in or implemented with the switch fabric 146 in someembodiments. Where multiple storage clusters 161 occupy a rack, thecommunications interconnect 173 can be included in or implemented with atop of rack switch, in some embodiments. As illustrated in FIG. 2B,storage cluster 161 is enclosed within a single chassis 138. Externalport 176 is coupled to storage nodes 150 through communicationsinterconnect 173, while external port 174 is coupled directly to astorage node. External power port 178 is coupled to power distributionbus 172. Storage nodes 150 may include varying amounts and differingcapacities of non-volatile solid state storage 152 as described withreference to FIG. 2A. In addition, one or more storage nodes 150 may bea compute only storage node as illustrated in FIG. 2B. Authorities 168are implemented on the non-volatile solid state storage 152, for exampleas lists or other data structures stored in memory. In some embodimentsthe authorities are stored within the non-volatile solid state storage152 and supported by software executing on a controller or otherprocessor of the non-volatile solid state storage 152. In a furtherembodiment, authorities 168 are implemented on the storage nodes 150,for example as lists or other data structures stored in the memory 154and supported by software executing on the CPU 156 of the storage node150. Authorities 168 control how and where data is stored in thenon-volatile solid state storage 152 in some embodiments. This controlassists in determining which type of erasure coding scheme is applied tothe data, and which storage nodes 150 have which portions of the data.Each authority 168 may be assigned to a non-volatile solid state storage152. Each authority may control a range of inode numbers, segmentnumbers, or other data identifiers which are assigned to data by a filesystem, by the storage nodes 150, or by the non-volatile solid statestorage 152, in various embodiments.

Every piece of data, and every piece of metadata, has redundancy in thesystem in some embodiments. In addition, every piece of data and everypiece of metadata has an owner, which may be referred to as anauthority. If that authority is unreachable, for example through failureof a storage node, there is a plan of succession for how to find thatdata or that metadata. In various embodiments, there are redundantcopies of authorities 168. Authorities 168 have a relationship tostorage nodes 150 and non-volatile solid state storage 152 in someembodiments. Each authority 168, covering a range of data segmentnumbers or other identifiers of the data, may be assigned to a specificnon-volatile solid state storage 152. In some embodiments theauthorities 168 for all of such ranges are distributed over thenon-volatile solid state storage 152 of a storage cluster. Each storagenode 150 has a network port that provides access to the non-volatilesolid state storage(s) 152 of that storage node 150. Data can be storedin a segment, which is associated with a segment number and that segmentnumber is an indirection for a configuration of a RAID (redundant arrayof independent disks) stripe in some embodiments. The assignment and useof the authorities 168 thus establishes an indirection to data.Indirection may be referred to as the ability to reference dataindirectly, in this case via an authority 168, in accordance with someembodiments. A segment identifies a set of non-volatile solid statestorage 152 and a local identifier into the set of non-volatile solidstate storage 152 that may contain data. In some embodiments, the localidentifier is an offset into the device and may be reused sequentiallyby multiple segments. In other embodiments the local identifier isunique for a specific segment and never reused. The offsets in thenon-volatile solid state storage 152 are applied to locating data forwriting to or reading from the non-volatile solid state storage 152 (inthe form of a RAID stripe). Data is striped across multiple units ofnon-volatile solid state storage 152, which may include or be differentfrom the non-volatile solid state storage 152 having the authority 168for a particular data segment.

If there is a change in where a particular segment of data is located,e.g., during a data move or a data reconstruction, the authority 168 forthat data segment should be consulted, at that non-volatile solid statestorage 152 or storage node 150 having that authority 168. In order tolocate a particular piece of data, embodiments calculate a hash valuefor a data segment or apply an inode number or a data segment number.The output of this operation points to a non-volatile solid statestorage 152 having the authority 168 for that particular piece of data.In some embodiments there are two stages to this operation. The firststage maps an entity identifier (ID), e.g., a segment number, inodenumber, or directory number to an authority identifier. This mapping mayinclude a calculation such as a hash or a bit mask. The second stage ismapping the authority identifier to a particular non-volatile solidstate storage 152, which may be done through an explicit mapping. Theoperation is repeatable, so that when the calculation is performed, theresult of the calculation repeatably and reliably points to a particularnon-volatile solid state storage 152 having that authority 168. Theoperation may include the set of reachable storage nodes as input. Ifthe set of reachable non-volatile solid state storage units changes theoptimal set changes. In some embodiments, the persisted value is thecurrent assignment (which is always true) and the calculated value isthe target assignment the cluster will attempt to reconfigure towards.This calculation may be used to determine the optimal non-volatile solidstate storage 152 for an authority in the presence of a set ofnon-volatile solid state storage 152 that are reachable and constitutethe same cluster. The calculation also determines an ordered set of peernon-volatile solid state storage 152 that will also record the authorityto non-volatile solid state storage mapping so that the authority may bedetermined even if the assigned non-volatile solid state storage isunreachable. A duplicate or substitute authority 168 may be consulted ifa specific authority 168 is unavailable in some embodiments.

With reference to FIGS. 2A and 2B, two of the many tasks of the CPU 156on a storage node 150 are to break up write data, and reassemble readdata. When the system has determined that data is to be written, theauthority 168 for that data is located as above. When the segment ID fordata is already determined the request to write is forwarded to thenon-volatile solid state storage 152 currently determined to be the hostof the authority 168 determined from the segment. The host CPU 156 ofthe storage node 150, on which the non-volatile solid state storage 152and corresponding authority 168 reside, then breaks up or shards thedata and transmits the data out to various non-volatile solid statestorage 152. The transmitted data is written as a data stripe inaccordance with an erasure coding scheme. In some embodiments, data isrequested to be pulled, and in other embodiments, data is pushed. Inreverse, when data is read, the authority 168 for the segment IDcontaining the data is located as described above. The host CPU 156 ofthe storage node 150 on which the non-volatile solid state storage 152and corresponding authority 168 reside requests the data from thenon-volatile solid state storage and corresponding storage nodes pointedto by the authority. In some embodiments the data is read from flashstorage as a data stripe. The host CPU 156 of storage node 150 thenreassembles the read data, correcting any errors (if present) accordingto the appropriate erasure coding scheme, and forwards the reassembleddata to the network. In further embodiments, some or all of these taskscan be handled in the non-volatile solid state storage 152. In someembodiments, the segment host requests the data be sent to storage node150 by requesting pages from storage and then sending the data to thestorage node making the original request.

In embodiments, authorities 168 operate to determine how operations willproceed against particular logical elements. Each of the logicalelements may be operated on through a particular authority across aplurality of storage controllers of a storage system. The authorities168 may communicate with the plurality of storage controllers so thatthe plurality of storage controllers collectively perform operationsagainst those particular logical elements.

In embodiments, logical elements could be, for example, files,directories, object buckets, individual objects, delineated parts offiles or objects, other forms of key-value pair databases, or tables. Inembodiments, performing an operation can involve, for example, ensuringconsistency, structural integrity, and/or recoverability with otheroperations against the same logical element, reading metadata and dataassociated with that logical element, determining what data should bewritten durably into the storage system to persist any changes for theoperation, or where metadata and data can be determined to be storedacross modular storage devices attached to a plurality of the storagecontrollers in the storage system.

In some embodiments the operations are token based transactions toefficiently communicate within a distributed system. Each transactionmay be accompanied by or associated with a token, which gives permissionto execute the transaction. The authorities 168 are able to maintain apre-transaction state of the system until completion of the operation insome embodiments. The token based communication may be accomplishedwithout a global lock across the system, and also enables restart of anoperation in case of a disruption or other failure.

In some systems, for example in UNIX-style file systems, data is handledwith an index node or inode, which specifies a data structure thatrepresents an object in a file system. The object could be a file or adirectory, for example. Metadata may accompany the object, as attributessuch as permission data and a creation timestamp, among otherattributes. A segment number could be assigned to all or a portion ofsuch an object in a file system. In other systems, data segments arehandled with a segment number assigned elsewhere. For purposes ofdiscussion, the unit of distribution is an entity, and an entity can bea file, a directory or a segment. That is, entities are units of data ormetadata stored by a storage system. Entities are grouped into setscalled authorities. Each authority has an authority owner, which is astorage node that has the exclusive right to update the entities in theauthority. In other words, a storage node contains the authority, andthat the authority, in turn, contains entities.

A segment is a logical container of data in accordance with someembodiments. A segment is an address space between medium address spaceand physical flash locations, i.e., the data segment number, are in thisaddress space. Segments may also contain meta-data, which enable dataredundancy to be restored (rewritten to different flash locations ordevices) without the involvement of higher level software. In oneembodiment, an internal format of a segment contains client data andmedium mappings to determine the position of that data. Each datasegment is protected, e.g., from memory and other failures, by breakingthe segment into a number of data and parity shards, where applicable.The data and parity shards are distributed, i.e., striped, acrossnon-volatile solid state storage 152 coupled to the host CPUs 156 (SeeFIGS. 2E and 2G) in accordance with an erasure coding scheme. Usage ofthe term segments refers to the container and its place in the addressspace of segments in some embodiments. Usage of the term stripe refersto the same set of shards as a segment and includes how the shards aredistributed along with redundancy or parity information in accordancewith some embodiments.

A series of address-space transformations takes place across an entirestorage system. At the top are the directory entries (file names) whichlink to an inode. Inodes point into medium address space, where data islogically stored. Medium addresses may be mapped through a series ofindirect mediums to spread the load of large files, or implement dataservices like deduplication or snapshots. Medium addresses may be mappedthrough a series of indirect mediums to spread the load of large files,or implement data services like deduplication or snapshots. Segmentaddresses are then translated into physical flash locations. Physicalflash locations have an address range bounded by the amount of flash inthe system in accordance with some embodiments. Medium addresses andsegment addresses are logical containers, and in some embodiments use a128 bit or larger identifier so as to be practically infinite, with alikelihood of reuse calculated as longer than the expected life of thesystem. Addresses from logical containers are allocated in ahierarchical fashion in some embodiments. Initially, each non-volatilesolid state storage 152 unit may be assigned a range of address space.Within this assigned range, the non-volatile solid state storage 152 isable to allocate addresses without synchronization with othernon-volatile solid state storage 152.

Data and metadata is stored by a set of underlying storage layouts thatare optimized for varying workload patterns and storage devices. Theselayouts incorporate multiple redundancy schemes, compression formats andindex algorithms. Some of these layouts store information aboutauthorities and authority masters, while others store file metadata andfile data. The redundancy schemes include error correction codes thattolerate corrupted bits within a single storage device (such as a NANDflash chip), erasure codes that tolerate the failure of multiple storagenodes, and replication schemes that tolerate data center or regionalfailures. In some embodiments, low density parity check (‘LDPC’) code isused within a single storage unit. Reed-Solomon encoding is used withina storage cluster, and mirroring is used within a storage grid in someembodiments. Metadata may be stored using an ordered log structuredindex (such as a Log Structured Merge Tree), and large data may not bestored in a log structured layout.

In order to maintain consistency across multiple copies of an entity,the storage nodes agree implicitly on two things through calculations:(1) the authority that contains the entity, and (2) the storage nodethat contains the authority. The assignment of entities to authoritiescan be done by pseudo randomly assigning entities to authorities, bysplitting entities into ranges based upon an externally produced key, orby placing a single entity into each authority. Examples of pseudorandomschemes are linear hashing and the Replication Under Scalable Hashing(‘RUSH’) family of hashes, including Controlled Replication UnderScalable Hashing (‘CRUSH’). In some embodiments, pseudo-randomassignment is utilized only for assigning authorities to nodes becausethe set of nodes can change. The set of authorities cannot change so anysubjective function may be applied in these embodiments. Some placementschemes automatically place authorities on storage nodes, while otherplacement schemes rely on an explicit mapping of authorities to storagenodes. In some embodiments, a pseudorandom scheme is utilized to mapfrom each authority to a set of candidate authority owners. Apseudorandom data distribution function related to CRUSH may assignauthorities to storage nodes and create a list of where the authoritiesare assigned. Each storage node has a copy of the pseudorandom datadistribution function, and can arrive at the same calculation fordistributing, and later finding or locating an authority. Each of thepseudorandom schemes requires the reachable set of storage nodes asinput in some embodiments in order to conclude the same target nodes.Once an entity has been placed in an authority, the entity may be storedon physical devices so that no expected failure will lead to unexpecteddata loss. In some embodiments, rebalancing algorithms attempt to storethe copies of all entities within an authority in the same layout and onthe same set of machines.

Examples of expected failures include device failures, stolen machines,datacenter fires, and regional disasters, such as nuclear or geologicalevents. Different failures lead to different levels of acceptable dataloss. In some embodiments, a stolen storage node impacts neither thesecurity nor the reliability of the system, while depending on systemconfiguration, a regional event could lead to no loss of data, a fewseconds or minutes of lost updates, or even complete data loss.

In the embodiments, the placement of data for storage redundancy isindependent of the placement of authorities for data consistency. Insome embodiments, storage nodes that contain authorities do not containany persistent storage. Instead, the storage nodes are connected tonon-volatile solid state storage units that do not contain authorities.The communications interconnect between storage nodes and non-volatilesolid state storage units consists of multiple communicationtechnologies and has non-uniform performance and fault tolerancecharacteristics. In some embodiments, as mentioned above, non-volatilesolid state storage units are connected to storage nodes via PCIexpress, storage nodes are connected together within a single chassisusing Ethernet backplane, and chassis are connected together to form astorage cluster. Storage clusters are connected to clients usingEthernet or fiber channel in some embodiments. If multiple storageclusters are configured into a storage grid, the multiple storageclusters are connected using the Internet or other long-distancenetworking links, such as a “metro scale” link or private link that doesnot traverse the internet.

Authority owners have the exclusive right to modify entities, to migrateentities from one non-volatile solid state storage unit to anothernon-volatile solid state storage unit, and to add and remove copies ofentities. This allows for maintaining the redundancy of the underlyingdata. When an authority owner fails, is going to be decommissioned, oris overloaded, the authority is transferred to a new storage node.Transient failures make it non-trivial to ensure that all non-faultymachines agree upon the new authority location. The ambiguity thatarises due to transient failures can be achieved automatically by aconsensus protocol such as Paxos, hot-warm failover schemes, via manualintervention by a remote system administrator, or by a local hardwareadministrator (such as by physically removing the failed machine fromthe cluster, or pressing a button on the failed machine). In someembodiments, a consensus protocol is used, and failover is automatic. Iftoo many failures or replication events occur in too short a timeperiod, the system goes into a self-preservation mode and haltsreplication and data movement activities until an administratorintervenes in accordance with some embodiments.

As authorities are transferred between storage nodes and authorityowners update entities in their authorities, the system transfersmessages between the storage nodes and non-volatile solid state storageunits. With regard to persistent messages, messages that have differentpurposes are of different types. Depending on the type of the message,the system maintains different ordering and durability guarantees. Asthe persistent messages are being processed, the messages aretemporarily stored in multiple durable and non-durable storage hardwaretechnologies. In some embodiments, messages are stored in RAM, NVRAM andon NAND flash devices, and a variety of protocols are used in order tomake efficient use of each storage medium. Latency-sensitive clientrequests may be persisted in replicated NVRAM, and then later NAND,while background rebalancing operations are persisted directly to NAND.

Persistent messages are persistently stored prior to being transmitted.This allows the system to continue to serve client requests despitefailures and component replacement. Although many hardware componentscontain unique identifiers that are visible to system administrators,manufacturer, hardware supply chain and ongoing monitoring qualitycontrol infrastructure, applications running on top of theinfrastructure address virtualize addresses. These virtualized addressesdo not change over the lifetime of the storage system, regardless ofcomponent failures and replacements. This allows each component of thestorage system to be replaced over time without reconfiguration ordisruptions of client request processing, i.e., the system supportsnon-disruptive upgrades.

In some embodiments, the virtualized addresses are stored withsufficient redundancy. A continuous monitoring system correlateshardware and software status and the hardware identifiers. This allowsdetection and prediction of failures due to faulty components andmanufacturing details. The monitoring system also enables the proactivetransfer of authorities and entities away from impacted devices beforefailure occurs by removing the component from the critical path in someembodiments.

FIG. 2C is a multiple level block diagram, showing contents of a storagenode 150 and contents of a non-volatile solid state storage 152 of thestorage node 150. Data is communicated to and from the storage node 150by a network interface controller (‘NIC’) 202 in some embodiments. Eachstorage node 150 has a CPU 156, and one or more non-volatile solid statestorage 152, as discussed above. Moving down one level in FIG. 2C, eachnon-volatile solid state storage 152 has a relatively fast non-volatilesolid state memory, such as nonvolatile random access memory (‘NVRAM’)204, and flash memory 206. In some embodiments, NVRAM 204 may be acomponent that does not require program/erase cycles (DRAM, MRAM, PCM),and can be a memory that can support being written vastly more oftenthan the memory is read from. Moving down another level in FIG. 2C, theNVRAM 204 is implemented in one embodiment as high speed volatilememory, such as dynamic random access memory (DRAM) 216, backed up byenergy reserve 218. Energy reserve 218 provides sufficient electricalpower to keep the DRAM 216 powered long enough for contents to betransferred to the flash memory 206 in the event of power failure. Insome embodiments, energy reserve 218 is a capacitor, super-capacitor,battery, or other device, that supplies a suitable supply of energysufficient to enable the transfer of the contents of DRAM 216 to astable storage medium in the case of power loss. The flash memory 206 isimplemented as multiple flash dies 222, which may be referred to aspackages of flash dies 222 or an array of flash dies 222. It should beappreciated that the flash dies 222 could be packaged in any number ofways, with a single die per package, multiple dies per package (i.e.,multichip packages), in hybrid packages, as bare dies on a printedcircuit board or other substrate, as encapsulated dies, etc. In theembodiment shown, the non-volatile solid state storage 152 has acontroller 212 or other processor, and an input output (I/O) port 210coupled to the controller 212. I/O port 210 is coupled to the CPU 156and/or the network interface controller 202 of the flash storage node150. Flash input output (I/O) port 220 is coupled to the flash dies 222,and a direct memory access unit (DMA) 214 is coupled to the controller212, the DRAM 216 and the flash dies 222. In the embodiment shown, theI/O port 210, controller 212, DMA unit 214 and flash I/O port 220 areimplemented on a programmable logic device (‘PLD’) 208, e.g., an FPGA.In this embodiment, each flash die 222 has pages, organized as sixteenkB (kilobyte) pages 224, and a register 226 through which data can bewritten to or read from the flash die 222. In further embodiments, othertypes of solid-state memory are used in place of, or in addition toflash memory illustrated within flash die 222.

Storage clusters 161, in various embodiments as disclosed herein, can becontrasted with storage arrays in general. The storage nodes 150 arepart of a collection that creates the storage cluster 161. Each storagenode 150 owns a slice of data and computing required to provide thedata. Multiple storage nodes 150 cooperate to store and retrieve thedata. Storage memory or storage devices, as used in storage arrays ingeneral, are less involved with processing and manipulating the data.Storage memory or storage devices in a storage array receive commands toread, write, or erase data. The storage memory or storage devices in astorage array are not aware of a larger system in which they areembedded, or what the data means. Storage memory or storage devices instorage arrays can include various types of storage memory, such as RAM,solid state drives, hard disk drives, etc. The non-volatile solid statestorage 152 units described herein have multiple interfaces activesimultaneously and serving multiple purposes. In some embodiments, someof the functionality of a storage node 150 is shifted into a storageunit 152, transforming the storage unit 152 into a combination ofstorage unit 152 and storage node 150. Placing computing (relative tostorage data) into the storage unit 152 places this computing closer tothe data itself. The various system embodiments have a hierarchy ofstorage node layers with different capabilities. By contrast, in astorage array, a controller owns and knows everything about all of thedata that the controller manages in a shelf or storage devices. In astorage cluster 161, as described herein, multiple controllers inmultiple non-volatile sold state storage 152 units and/or storage nodes150 cooperate in various ways (e.g., for erasure coding, data sharding,metadata communication and redundancy, storage capacity expansion orcontraction, data recovery, and so on).

FIG. 2D shows a storage server environment, which uses embodiments ofthe storage nodes 150 and storage 152 units of FIGS. 2A-C. In thisversion, each non-volatile solid state storage 152 unit has a processorsuch as controller 212 (see FIG. 2C), an FPGA, flash memory 206, andNVRAM 204 (which is super-capacitor backed DRAM 216, see FIGS. 2B and2C) on a PCIe (peripheral component interconnect express) board in achassis 138 (see FIG. 2A). The non-volatile solid state storage 152 unitmay be implemented as a single board containing storage, and may be thelargest tolerable failure domain inside the chassis. In someembodiments, up to two non-volatile solid state storage 152 units mayfail and the device will continue with no data loss.

The physical storage is divided into named regions based on applicationusage in some embodiments. The NVRAM 204 is a contiguous block ofreserved memory in the non-volatile solid state storage 152 DRAM 216,and is backed by NAND flash. NVRAM 204 is logically divided intomultiple memory regions written for two as spool (e.g., spool region).Space within the NVRAM 204 spools is managed by each authority 168independently. Each device provides an amount of storage space to eachauthority 168. That authority 168 further manages lifetimes andallocations within that space. Examples of a spool include distributedtransactions or notions. When the primary power to a non-volatile solidstate storage 152 unit fails, onboard super-capacitors provide a shortduration of power hold up. During this holdup interval, the contents ofthe NVRAM 204 are flushed to flash memory 206. On the next power-on, thecontents of the NVRAM 204 are recovered from the flash memory 206.

As for the storage unit controller, the responsibility of the logical“controller” is distributed across each of the blades containingauthorities 168. This distribution of logical control is shown in FIG.2D as a host controller 242, mid-tier controller 244 and storage unitcontroller(s) 246. Management of the control plane and the storage planeare treated independently, although parts may be physically co-locatedon the same blade. Each authority 168 effectively serves as anindependent controller. Each authority 168 provides its own data andmetadata structures, its own background workers, and maintains its ownlifecycle.

FIG. 2E is a blade 252 hardware block diagram, showing a control plane254, compute and storage planes 256, 258, and authorities 168interacting with underlying physical resources, using embodiments of thestorage nodes 150 and storage units 152 of FIGS. 2A-C in the storageserver environment of FIG. 2D. The control plane 254 is partitioned intoa number of authorities 168 which can use the compute resources in thecompute plane 256 to run on any of the blades 252. The storage plane 258is partitioned into a set of devices, each of which provides access toflash 206 and NVRAM 204 resources. In one embodiment, the compute plane256 may perform the operations of a storage array controller, asdescribed herein, on one or more devices of the storage plane 258 (e.g.,a storage array).

In the compute and storage planes 256, 258 of FIG. 2E, the authorities168 interact with the underlying physical resources (i.e., devices).From the point of view of an authority 168, its resources are stripedover all of the physical devices. From the point of view of a device, itprovides resources to all authorities 168, irrespective of where theauthorities happen to run. Each authority 168 has allocated or has beenallocated one or more partitions 260 of storage memory in the storageunits 152, e.g., partitions 260 in flash memory 206 and NVRAM 204. Eachauthority 168 uses those allocated partitions 260 that belong to it, forwriting or reading user data. Authorities can be associated withdiffering amounts of physical storage of the system. For example, oneauthority 168 could have a larger number of partitions 260 or largersized partitions 260 in one or more storage units 152 than one or moreother authorities 168.

FIG. 2F depicts elasticity software layers in blades 252 of a storagecluster, in accordance with some embodiments. In the elasticitystructure, elasticity software is symmetric, i.e., each blade's computemodule 270 runs the three identical layers of processes depicted in FIG.2F. Storage managers 274 execute read and write requests from otherblades 252 for data and metadata stored in local storage unit 152 NVRAM204 and flash 206. Authorities 168 fulfill client requests by issuingthe necessary reads and writes to the blades 252 on whose storage units152 the corresponding data or metadata resides. Endpoints 272 parseclient connection requests received from switch fabric 146 supervisorysoftware, relay the client connection requests to the authorities 168responsible for fulfillment, and relay the authorities' 168 responses toclients. The symmetric three-layer structure enables the storagesystem's high degree of concurrency. Elasticity scales out efficientlyand reliably in these embodiments. In addition, elasticity implements aunique scale-out technique that balances work evenly across allresources regardless of client access pattern, and maximizes concurrencyby eliminating much of the need for inter-blade coordination thattypically occurs with conventional distributed locking.

Still referring to FIG. 2F, authorities 168 running in the computemodules 270 of a blade 252 perform the internal operations required tofulfill client requests. One feature of elasticity is that authorities168 are stateless, i.e., they cache active data and metadata in theirown blades' 252 DRAMs for fast access, but the authorities store everyupdate in their NVRAM 204 partitions on three separate blades 252 untilthe update has been written to flash 206. All the storage system writesto NVRAM 204 are in triplicate to partitions on three separate blades252 in some embodiments. With triple-mirrored NVRAM 204 and persistentstorage protected by parity and Reed-Solomon RAID checksums, the storagesystem can survive concurrent failure of two blades 252 with no loss ofdata, metadata, or access to either.

Because authorities 168 are stateless, they can migrate between blades252. Each authority 168 has a unique identifier. NVRAM 204 and flash 206partitions are associated with authorities' 168 identifiers, not withthe blades 252 on which they are running in some. Thus, when anauthority 168 migrates, the authority 168 continues to manage the samestorage partitions from its new location. When a new blade 252 isinstalled in an embodiment of the storage cluster, the systemautomatically rebalances load by: partitioning the new blade's 252storage for use by the system's authorities 168, migrating selectedauthorities 168 to the new blade 252, starting endpoints 272 on the newblade 252 and including them in the switch fabric's 146 clientconnection distribution algorithm.

From their new locations, migrated authorities 168 persist the contentsof their NVRAM 204 partitions on flash 206, process read and writerequests from other authorities 168, and fulfill the client requeststhat endpoints 272 direct to them. Similarly, if a blade 252 fails or isremoved, the system redistributes its authorities 168 among the system'sremaining blades 252. The redistributed authorities 168 continue toperform their original functions from their new locations.

FIG. 2G depicts authorities 168 and storage resources in blades 252 of astorage cluster, in accordance with some embodiments. Each authority 168is exclusively responsible for a partition of the flash 206 and NVRAM204 on each blade 252. The authority 168 manages the content andintegrity of its partitions independently of other authorities 168.Authorities 168 compress incoming data and preserve it temporarily intheir NVRAM 204 partitions, and then consolidate, RAID-protect, andpersist the data in segments of the storage in their flash 206partitions. As the authorities 168 write data to flash 206, storagemanagers 274 perform the necessary flash translation to optimize writeperformance and maximize media longevity. In the background, authorities168 “garbage collect,” or reclaim space occupied by data that clientshave made obsolete by overwriting the data. It should be appreciatedthat since authorities' 168 partitions are disjoint, there is no needfor distributed locking to execute client and writes or to performbackground functions.

The embodiments described herein may utilize various software,communication and/or networking protocols. In addition, theconfiguration of the hardware and/or software may be adjusted toaccommodate various protocols. For example, the embodiments may utilizeActive Directory, which is a database based system that providesauthentication, directory, policy, and other services in a WINDOWS™environment. In these embodiments, LDAP (Lightweight Directory AccessProtocol) is one example application protocol for querying and modifyingitems in directory service providers such as Active Directory. In someembodiments, a network lock manager (‘NLM’) is utilized as a facilitythat works in cooperation with the Network File System (‘NFS’) toprovide a System V style of advisory file and record locking over anetwork. The Server Message Block (‘SMB’) protocol, one version of whichis also known as Common Internet File System (‘CIFS’), may be integratedwith the storage systems discussed herein. SMP operates as anapplication-layer network protocol typically used for providing sharedaccess to files, printers, and serial ports and miscellaneouscommunications between nodes on a network. SMB also provides anauthenticated inter-process communication mechanism. AMAZON™ S3 (SimpleStorage Service) is a web service offered by Amazon Web Services, andthe systems described herein may interface with Amazon S3 through webservices interfaces (REST (representational state transfer), SOAP(simple object access protocol), and BitTorrent). A RESTful API(application programming interface) breaks down a transaction to createa series of small modules. Each module addresses a particular underlyingpart of the transaction. The control or permissions provided with theseembodiments, especially for object data, may include utilization of anaccess control list (‘ACL’). The ACL is a list of permissions attachedto an object and the ACL specifies which users or system processes aregranted access to objects, as well as what operations are allowed ongiven objects. The systems may utilize Internet Protocol version 6(‘IPv6’), as well as IPv4, for the communications protocol that providesan identification and location system for computers on networks androutes traffic across the Internet. The routing of packets betweennetworked systems may include Equal-cost multi-path routing (‘ECMP’),which is a routing strategy where next-hop packet forwarding to a singledestination can occur over multiple “best paths” which tie for top placein routing metric calculations. Multi-path routing can be used inconjunction with most routing protocols, because it is a per-hopdecision limited to a single router. The software may supportMulti-tenancy, which is an architecture in which a single instance of asoftware application serves multiple customers. Each customer may bereferred to as a tenant. Tenants may be given the ability to customizesome parts of the application, but may not customize the application'scode, in some embodiments. The embodiments may maintain audit logs. Anaudit log is a document that records an event in a computing system. Inaddition to documenting what resources were accessed, audit log entriestypically include destination and source addresses, a timestamp, anduser login information for compliance with various regulations. Theembodiments may support various key management policies, such asencryption key rotation. In addition, the system may support dynamicroot passwords or some variation dynamically changing passwords.

FIG. 3A sets forth a diagram of a storage system 306 that is coupled fordata communications with a cloud services provider 302 in accordancewith some embodiments of the present disclosure. Although depicted inless detail, the storage system 306 depicted in FIG. 3A may be similarto the storage systems described above with reference to FIGS. 1A-1D andFIGS. 2A-2G. In some embodiments, the storage system 306 depicted inFIG. 3A may be embodied as a storage system that includes imbalancedactive/active controllers, as a storage system that includes balancedactive/active controllers, as a storage system that includesactive/active controllers where less than all of each controller'sresources are utilized such that each controller has reserve resourcesthat may be used to support failover, as a storage system that includesfully active/active controllers, as a storage system that includesdataset-segregated controllers, as a storage system that includesdual-layer architectures with front-end controllers and back-endintegrated storage controllers, as a storage system that includesscale-out clusters of dual-controller arrays, as well as combinations ofsuch embodiments.

In the example depicted in FIG. 3A, the storage system 306 is coupled tothe cloud services provider 302 via a data communications link 304. Thedata communications link 304 may be embodied as a dedicated datacommunications link, as a data communications pathway that is providedthrough the use of one or data communications networks such as a widearea network (‘WAN’) or LAN, or as some other mechanism capable oftransporting digital information between the storage system 306 and thecloud services provider 302. Such a data communications link 304 may befully wired, fully wireless, or some aggregation of wired and wirelessdata communications pathways. In such an example, digital informationmay be exchanged between the storage system 306 and the cloud servicesprovider 302 via the data communications link 304 using one or more datacommunications protocols. For example, digital information may beexchanged between the storage system 306 and the cloud services provider302 via the data communications link 304 using the handheld devicetransfer protocol (‘HDTP’), hypertext transfer protocol (‘HTTP’),internet protocol (‘IP’), real-time transfer protocol (‘RTP’),transmission control protocol (‘TCP’), user datagram protocol (‘UDP’),wireless application protocol (‘WAP’), or other protocol.

The cloud services provider 302 depicted in FIG. 3A may be embodied, forexample, as a system and computing environment that provides a vastarray of services to users of the cloud services provider 302 throughthe sharing of computing resources via the data communications link 304.The cloud services provider 302 may provide on-demand access to a sharedpool of configurable computing resources such as computer networks,servers, storage, applications and services, and so on. The shared poolof configurable resources may be rapidly provisioned and released to auser of the cloud services provider 302 with minimal management effort.Generally, the user of the cloud services provider 302 is unaware of theexact computing resources utilized by the cloud services provider 302 toprovide the services. Although in many cases such a cloud servicesprovider 302 may be accessible via the Internet, readers of skill in theart will recognize that any system that abstracts the use of sharedresources to provide services to a user through any data communicationslink may be considered a cloud services provider 302.

In the example depicted in FIG. 3A, the cloud services provider 302 maybe configured to provide a variety of services to the storage system 306and users of the storage system 306 through the implementation ofvarious service models. For example, the cloud services provider 302 maybe configured to provide services through the implementation of aninfrastructure as a service (‘IaaS’) service model, through theimplementation of a platform as a service (‘PaaS’) service model,through the implementation of a software as a service (‘SaaS’) servicemodel, through the implementation of an authentication as a service(‘AaaS’) service model, through the implementation of a storage as aservice model where the cloud services provider 302 offers access to itsstorage infrastructure for use by the storage system 306 and users ofthe storage system 306, and so on. Readers will appreciate that thecloud services provider 302 may be configured to provide additionalservices to the storage system 306 and users of the storage system 306through the implementation of additional service models, as the servicemodels described above are included only for explanatory purposes and inno way represent a limitation of the services that may be offered by thecloud services provider 302 or a limitation as to the service modelsthat may be implemented by the cloud services provider 302.

In the example depicted in FIG. 3A, the cloud services provider 302 maybe embodied, for example, as a private cloud, as a public cloud, or as acombination of a private cloud and public cloud. In an embodiment inwhich the cloud services provider 302 is embodied as a private cloud,the cloud services provider 302 may be dedicated to providing servicesto a single organization rather than providing services to multipleorganizations. In an embodiment where the cloud services provider 302 isembodied as a public cloud, the cloud services provider 302 may provideservices to multiple organizations. In still alternative embodiments,the cloud services provider 302 may be embodied as a mix of a privateand public cloud services with a hybrid cloud deployment.

Although not explicitly depicted in FIG. 3A, readers will appreciatethat a vast amount of additional hardware components and additionalsoftware components may be necessary to facilitate the delivery of cloudservices to the storage system 306 and users of the storage system 306.For example, the storage system 306 may be coupled to (or even include)a cloud storage gateway. Such a cloud storage gateway may be embodied,for example, as hardware-based or software-based appliance that islocated on premise with the storage system 306. Such a cloud storagegateway may operate as a bridge between local applications that areexecuting on the storage system 306 and remote, cloud-based storage thatis utilized by the storage system 306. Through the use of a cloudstorage gateway, organizations may move primary iSCSI or NAS to thecloud services provider 302, thereby enabling the organization to savespace on their on-premises storage systems. Such a cloud storage gatewaymay be configured to emulate a disk array, a block-based device, a fileserver, or other storage system that can translate the SCSI commands,file server commands, or other appropriate command into REST-spaceprotocols that facilitate communications with the cloud servicesprovider 302.

In order to enable the storage system 306 and users of the storagesystem 306 to make use of the services provided by the cloud servicesprovider 302, a cloud migration process may take place during whichdata, applications, or other elements from an organization's localsystems (or even from another cloud environment) are moved to the cloudservices provider 302. In order to successfully migrate data,applications, or other elements to the cloud services provider's 302environment, middleware such as a cloud migration tool may be utilizedto bridge gaps between the cloud services provider's 302 environment andan organization's environment. Such cloud migration tools may also beconfigured to address potentially high network costs and long transfertimes associated with migrating large volumes of data to the cloudservices provider 302, as well as addressing security concernsassociated with sensitive data to the cloud services provider 302 overdata communications networks. In order to further enable the storagesystem 306 and users of the storage system 306 to make use of theservices provided by the cloud services provider 302, a cloudorchestrator may also be used to arrange and coordinate automated tasksin pursuit of creating a consolidated process or workflow. Such a cloudorchestrator may perform tasks such as configuring various components,whether those components are cloud components or on-premises components,as well as managing the interconnections between such components. Thecloud orchestrator can simplify the inter-component communication andconnections to ensure that links are correctly configured andmaintained.

In the example depicted in FIG. 3A, and as described briefly above, thecloud services provider 302 may be configured to provide services to thestorage system 306 and users of the storage system 306 through the usageof a SaaS service model, eliminating the need to install and run theapplication on local computers, which may simplify maintenance andsupport of the application. Such applications may take many forms inaccordance with various embodiments of the present disclosure. Forexample, the cloud services provider 302 may be configured to provideaccess to data analytics applications to the storage system 306 andusers of the storage system 306. Such data analytics applications may beconfigured, for example, to receive vast amounts of telemetry dataphoned home by the storage system 306. Such telemetry data may describevarious operating characteristics of the storage system 306 and may beanalyzed for a vast array of purposes including, for example, todetermine the health of the storage system 306, to identify workloadsthat are executing on the storage system 306, to predict when thestorage system 306 will run out of various resources, to recommendconfiguration changes, hardware or software upgrades, workflowmigrations, or other actions that may improve the operation of thestorage system 306.

The cloud services provider 302 may also be configured to provide accessto virtualized computing environments to the storage system 306 andusers of the storage system 306. Such virtualized computing environmentsmay be embodied, for example, as a virtual machine or other virtualizedcomputer hardware platforms, virtual storage devices, virtualizedcomputer network resources, and so on. Examples of such virtualizedenvironments can include virtual machines that are created to emulate anactual computer, virtualized desktop environments that separate alogical desktop from a physical machine, virtualized file systems thatallow uniform access to different types of concrete file systems, andmany others.

Although the example depicted in FIG. 3A illustrates the storage system306 being coupled for data communications with the cloud servicesprovider 302, in other embodiments the storage system 306 may be part ofa hybrid cloud deployment in which private cloud elements (e.g., privatecloud services, on-premises infrastructure, and so on) and public cloudelements (e.g., public cloud services, infrastructure, and so on thatmay be provided by one or more cloud services providers) are combined toform a single solution, with orchestration among the various platforms.Such a hybrid cloud deployment may leverage hybrid cloud managementsoftware such as, for example, Azure™ Arc from Microsoft™, thatcentralize the management of the hybrid cloud deployment to anyinfrastructure and enable the deployment of services anywhere. In suchan example, the hybrid cloud management software may be configured tocreate, update, and delete resources (both physical and virtual) thatform the hybrid cloud deployment, to allocate compute and storage tospecific workloads, to monitor workloads and resources for performance,policy compliance, updates and patches, security status, or to perform avariety of other tasks.

Readers will appreciate that by pairing the storage systems describedherein with one or more cloud services providers, various offerings maybe enabled. For example, disaster recovery as a service (‘DRaaS’) may beprovided where cloud resources are utilized to protect applications anddata from disruption caused by disaster, including in embodiments wherethe storage systems may serve as the primary data store. In suchembodiments, a total system backup may be taken that allows for businesscontinuity in the event of system failure. In such embodiments, clouddata backup techniques (by themselves or as part of a larger DRaaSsolution) may also be integrated into an overall solution that includesthe storage systems and cloud services providers described herein.

The storage systems described herein, as well as the cloud servicesproviders, may be utilized to provide a wide array of security features.For example, the storage systems may encrypt data at rest (and data maybe sent to and from the storage systems encrypted) and may make use ofKey Management-as-a-Service (‘KMaaS’) to manage encryption keys, keysfor locking and unlocking storage devices, and so on. Likewise, clouddata security gateways or similar mechanisms may be utilized to ensurethat data stored within the storage systems does not improperly end upbeing stored in the cloud as part of a cloud data backup operation.Furthermore, microsegmentation or identity-based-segmentation may beutilized in a data center that includes the storage systems or withinthe cloud services provider, to create secure zones in data centers andcloud deployments that enables the isolation of workloads from oneanother.

For further explanation, FIG. 3B sets forth a diagram of a storagesystem 306 in accordance with some embodiments of the presentdisclosure. Although depicted in less detail, the storage system 306depicted in FIG. 3B may be similar to the storage systems describedabove with reference to FIGS. 1A-1D and FIGS. 2A-2G as the storagesystem may include many of the components described above.

The storage system 306 depicted in FIG. 3B may include a vast amount ofstorage resources 308, which may be embodied in many forms. For example,the storage resources 308 can include nano-RAM or another form ofnonvolatile random access memory that utilizes carbon nanotubesdeposited on a substrate, 3D crosspoint non-volatile memory, flashmemory including single-level cell (‘SLC’) NAND flash, multi-level cell(‘MLC’) NAND flash, triple-level cell (‘TLC’) NAND flash, quad-levelcell (‘QLC’) NAND flash, or others. Likewise, the storage resources 308may include non-volatile magnetoresistive random-access memory (‘MRAM’),including spin transfer torque (STY) MRAM. The example storage resources308 may alternatively include non-volatile phase-change memory (‘PCM’),quantum memory that allows for the storage and retrieval of photonicquantum information, resistive random-access memory (‘ReRAM’), storageclass memory (‘SCM’), or other form of storage resources, including anycombination of resources described herein. Readers will appreciate thatother forms of computer memories and storage devices may be utilized bythe storage systems described above, including DRAM, SRAM, EEPROM,universal memory, and many others. The storage resources 308 depicted inFIG. 3A may be embodied in a variety of form factors, including but notlimited to, dual in-line memory modules (‘DIMMs’), non-volatile dualin-line memory modules (‘NVDIMMs’), M.2, U.2, and others.

The storage resources 308 depicted in FIG. 3B may include various formsof SCM. SCM may effectively treat fast, non-volatile memory (e.g., NANDflash) as an extension of DRAM such that an entire dataset may betreated as an in-memory dataset that resides entirely in DRAM. SCM mayinclude non-volatile media such as, for example, NAND flash. Such NANDflash may be accessed utilizing NVMe that can use the PCIe bus as itstransport, providing for relatively low access latencies compared toolder protocols. In fact, the network protocols used for SSDs inall-flash arrays can include NVMe using Ethernet (ROCE, NVME TCP), FibreChannel (NVMe FC), InfiniBand (iWARP), and others that make it possibleto treat fast, non-volatile memory as an extension of DRAM. In view ofthe fact that DRAM is often byte-addressable and fast, non-volatilememory such as NAND flash is block-addressable, a controllersoftware/hardware stack may be needed to convert the block data to thebytes that are stored in the media. Examples of media and software thatmay be used as SCM can include, for example, 3D XPoint, Intel MemoryDrive Technology, Samsung's Z-SSD, and others.

The storage resources 308 depicted in FIG. 3B may also include racetrackmemory (also referred to as domain-wall memory). Such racetrack memorymay be embodied as a form of non-volatile, solid-state memory thatrelies on the intrinsic strength and orientation of the magnetic fieldcreated by an electron as it spins in addition to its electronic charge,in solid-state devices. Through the use of spin-coherent electriccurrent to move magnetic domains along a nanoscopic permalloy wire, thedomains may pass by magnetic read/write heads positioned near the wireas current is passed through the wire, which alter the domains to recordpatterns of bits. In order to create a racetrack memory device, manysuch wires and read/write elements may be packaged together.

The example storage system 306 depicted in FIG. 3B may implement avariety of storage architectures. For example, storage systems inaccordance with some embodiments of the present disclosure may utilizeblock storage where data is stored in blocks, and each block essentiallyacts as an individual hard drive. Storage systems in accordance withsome embodiments of the present disclosure may utilize object storage,where data is managed as objects. Each object may include the dataitself, a variable amount of metadata, and a globally unique identifier,where object storage can be implemented at multiple levels (e.g., devicelevel, system level, interface level). Storage systems in accordancewith some embodiments of the present disclosure utilize file storage inwhich data is stored in a hierarchical structure. Such data may be savedin files and folders, and presented to both the system storing it andthe system retrieving it in the same format.

The example storage system 306 depicted in FIG. 3B may be embodied as astorage system in which additional storage resources can be addedthrough the use of a scale-up model, additional storage resources can beadded through the use of a scale-out model, or through some combinationthereof. In a scale-up model, additional storage may be added by addingadditional storage devices. In a scale-out model, however, additionalstorage nodes may be added to a cluster of storage nodes, where suchstorage nodes can include additional processing resources, additionalnetworking resources, and so on.

The example storage system 306 depicted in FIG. 3B may leverage thestorage resources described above in a variety of different ways. Forexample, some portion of the storage resources may be utilized to serveas a write cache, storage resources within the storage system may beutilized as a read cache, or tiering may be achieved within the storagesystems by placing data within the storage system in accordance with oneor more tiering policies.

The storage system 306 depicted in FIG. 3B also includes communicationsresources 310 that may be useful in facilitating data communicationsbetween components within the storage system 306, as well as datacommunications between the storage system 306 and computing devices thatare outside of the storage system 306, including embodiments where thoseresources are separated by a relatively vast expanse. The communicationsresources 310 may be configured to utilize a variety of differentprotocols and data communication fabrics to facilitate datacommunications between components within the storage systems as well ascomputing devices that are outside of the storage system. For example,the communications resources 310 can include fibre channel (‘FC’)technologies such as FC fabrics and FC protocols that can transport SCSIcommands over FC network, FC over ethernet (‘FCoE’) technologies throughwhich FC frames are encapsulated and transmitted over Ethernet networks,InfiniBand (‘IB’) technologies in which a switched fabric topology isutilized to facilitate transmissions between channel adapters, NVMExpress (‘NVMe’) technologies and NVMe over fabrics (‘NVMeoF’)technologies through which non-volatile storage media attached via a PCIexpress (‘PCIe’) bus may be accessed, and others. In fact, the storagesystems described above may, directly or indirectly, make use ofneutrino communication technologies and devices through whichinformation (including binary information) is transmitted using a beamof neutrinos.

The communications resources 310 can also include mechanisms foraccessing storage resources 308 within the storage system 306 utilizingserial attached SCSI (‘SAS’), serial ATA (‘SATA’) bus interfaces forconnecting storage resources 308 within the storage system 306 to hostbus adapters within the storage system 306, internet small computersystems interface (‘iSCSI’) technologies to provide block-level accessto storage resources 308 within the storage system 306, and othercommunications resources that that may be useful in facilitating datacommunications between components within the storage system 306, as wellas data communications between the storage system 306 and computingdevices that are outside of the storage system 306.

The storage system 306 depicted in FIG. 3B also includes processingresources 312 that may be useful in useful in executing computer programinstructions and performing other computational tasks within the storagesystem 306. The processing resources 312 may include one or more ASICsthat are customized for some particular purpose as well as one or moreCPUs. The processing resources 312 may also include one or more DSPs,one or more FPGAs, one or more systems on a chip (‘SoCs’), or other formof processing resources 312. The storage system 306 may utilize thestorage resources 312 to perform a variety of tasks including, but notlimited to, supporting the execution of software resources 314 that willbe described in greater detail below.

The storage system 306 depicted in FIG. 3B also includes softwareresources 314 that, when executed by processing resources 312 within thestorage system 306, may perform a vast array of tasks. The softwareresources 314 may include, for example, one or more modules of computerprogram instructions that when executed by processing resources 312within the storage system 306 are useful in carrying out various dataprotection techniques. Such data protection techniques may be carriedout, for example, by system software executing on computer hardwarewithin the storage system, by a cloud services provider, or in otherways. Such data protection techniques can include data archiving, databackup, data replication, data snapshotting, data and database cloning,and other data protection techniques.

The software resources 314 may also include software that is useful inimplementing software-defined storage (‘SDS’). In such an example, thesoftware resources 314 may include one or more modules of computerprogram instructions that, when executed, are useful in policy-basedprovisioning and management of data storage that is independent of theunderlying hardware. Such software resources 314 may be useful inimplementing storage virtualization to separate the storage hardwarefrom the software that manages the storage hardware.

The software resources 314 may also include software that is useful infacilitating and optimizing I/O operations that are directed to thestorage system 306. For example, the software resources 314 may includesoftware modules that perform various data reduction techniques such as,for example, data compression, data deduplication, and others. Thesoftware resources 314 may include software modules that intelligentlygroup together I/O operations to facilitate better usage of theunderlying storage resource 308, software modules that perform datamigration operations to migrate from within a storage system, as well assoftware modules that perform other functions. Such software resources314 may be embodied as one or more software containers or in many otherways.

For further explanation, FIG. 3C sets forth an example of a cloud-basedstorage system 318 in accordance with some embodiments of the presentdisclosure. In the example depicted in FIG. 3C, the cloud-based storagesystem 318 is created entirely in a cloud computing environment 316 suchas, for example, Amazon Web Services (‘AWS’)™, Microsoft Azure™, GoogleCloud Platform™, IBM Cloud™, Oracle Cloud™, and others. The cloud-basedstorage system 318 may be used to provide services similar to theservices that may be provided by the storage systems described above.

The cloud-based storage system 318 depicted in FIG. 3C includes twocloud computing instances 320, 322 that each are used to support theexecution of a storage controller application 324, 326. The cloudcomputing instances 320, 322 may be embodied, for example, as instancesof cloud computing resources (e.g., virtual machines) that may beprovided by the cloud computing environment 316 to support the executionof software applications such as the storage controller application 324,326. For example, each of the cloud computing instances 320, 322 mayexecute on an Azure VM, where each Azure VM may include high speedtemporary storage that may be leveraged as a cache (e.g., as a readcache). In one embodiment, the cloud computing instances 320, 322 may beembodied as Amazon Elastic Compute Cloud (‘EC2’) instances. In such anexample, an Amazon Machine Image (‘AMI’) that includes the storagecontroller application 324, 326 may be booted to create and configure avirtual machine that may execute the storage controller application 324,326.

In the example method depicted in FIG. 3C, the storage controllerapplication 324, 326 may be embodied as a module of computer programinstructions that, when executed, carries out various storage tasks. Forexample, the storage controller application 324, 326 may be embodied asa module of computer program instructions that, when executed, carriesout the same tasks as the controllers 110A, 110B in FIG. 1A describedabove such as writing data to the cloud-based storage system 318,erasing data from the cloud-based storage system 318, retrieving datafrom the cloud-based storage system 318, monitoring and reporting ofdisk utilization and performance, performing redundancy operations, suchas RAID or RAID-like data redundancy operations, compressing data,encrypting data, deduplicating data, and so forth. Readers willappreciate that because there are two cloud computing instances 320, 322that each include the storage controller application 324, 326, in someembodiments one cloud computing instance 320 may operate as the primarycontroller as described above while the other cloud computing instance322 may operate as the secondary controller as described above. Readerswill appreciate that the storage controller application 324, 326depicted in FIG. 3C may include identical source code that is executedwithin different cloud computing instances 320, 322 such as distinct EC2instances.

Readers will appreciate that other embodiments that do not include aprimary and secondary controller are within the scope of the presentdisclosure. For example, each cloud computing instance 320, 322 mayoperate as a primary controller for some portion of the address spacesupported by the cloud-based storage system 318, each cloud computinginstance 320, 322 may operate as a primary controller where theservicing of I/O operations directed to the cloud-based storage system318 are divided in some other way, and so on. In fact, in otherembodiments where costs savings may be prioritized over performancedemands, only a single cloud computing instance may exist that containsthe storage controller application.

The cloud-based storage system 318 depicted in FIG. 3C includes cloudcomputing instances 340 a, 340 b, 340 n with local storage 330, 334,338. The cloud computing instances 340 a, 340 b, 340 n may be embodied,for example, as instances of cloud computing resources that may beprovided by the cloud computing environment 316 to support the executionof software applications. The cloud computing instances 340 a, 340 b,340 n of FIG. 3C may differ from the cloud computing instances 320, 322described above as the cloud computing instances 340 a, 340 b, 340 n ofFIG. 3C have local storage 330, 334, 338 resources whereas the cloudcomputing instances 320, 322 that support the execution of the storagecontroller application 324, 326 need not have local storage resources.The cloud computing instances 340 a, 340 b, 340 n with local storage330, 334, 338 may be embodied, for example, as EC2 M5 instances thatinclude one or more SSDs, as EC2 R5 instances that include one or moreSSDs, as EC2 I3 instances that include one or more SSDs, and so on. Insome embodiments, the local storage 330, 334, 338 must be embodied assolid-state storage (e.g., SSDs) rather than storage that makes use ofhard disk drives.

In the example depicted in FIG. 3C, each of the cloud computinginstances 340 a, 340 b, 340 n with local storage 330, 334, 338 caninclude a software daemon 328, 332, 336 that, when executed by a cloudcomputing instance 340 a, 340 b, 340 n can present itself to the storagecontroller applications 324, 326 as if the cloud computing instance 340a, 340 b, 340 n were a physical storage device (e.g., one or more SSDs).In such an example, the software daemon 328, 332, 336 may includecomputer program instructions similar to those that would normally becontained on a storage device such that the storage controllerapplications 324, 326 can send and receive the same commands that astorage controller would send to storage devices. In such a way, thestorage controller applications 324, 326 may include code that isidentical to (or substantially identical to) the code that would beexecuted by the controllers in the storage systems described above. Inthese and similar embodiments, communications between the storagecontroller applications 324, 326 and the cloud computing instances 340a, 340 b, 340 n with local storage 330, 334, 338 may utilize iSCSI, NVMeover TCP, messaging, a custom protocol, or in some other mechanism.

In the example depicted in FIG. 3C, each of the cloud computinginstances 340 a, 340 b, 340 n with local storage 330, 334, 338 may alsobe coupled to block storage 342, 344, 346 that is offered by the cloudcomputing environment 316 such as, for example, as Amazon Elastic BlockStore (‘EBS’) volumes. In such an example, the block storage 342, 344,346 that is offered by the cloud computing environment 316 may beutilized in a manner that is similar to how the NVRAM devices describedabove are utilized, as the software daemon 328, 332, 336 (or some othermodule) that is executing within a particular cloud comping instance 340a, 340 b, 340 n may, upon receiving a request to write data, initiate awrite of the data to its attached EBS volume as well as a write of thedata to its local storage 330, 334, 338 resources. In some alternativeembodiments, data may only be written to the local storage 330, 334, 338resources within a particular cloud comping instance 340 a, 340 b, 340n. In an alternative embodiment, rather than using the block storage342, 344, 346 that is offered by the cloud computing environment 316 asNVRAM, actual RAM on each of the cloud computing instances 340 a, 340 b,340 n with local storage 330, 334, 338 may be used as NVRAM, therebydecreasing network utilization costs that would be associated with usingan EBS volume as the NVRAM. In yet another embodiment, high performanceblock storage resources such as one or more Azure Ultra Disks may beutilized as the NVRAM.

The storage controller applications 324, 326 may be used to performvarious tasks such as deduplicating the data contained in the request,compressing the data contained in the request, determining where to thewrite the data contained in the request, and so on, before ultimatelysending a request to write a deduplicated, encrypted, or otherwisepossibly updated version of the data to one or more of the cloudcomputing instances 340 a, 340 b, 340 n with local storage 330, 334,338. Either cloud computing instance 320, 322, in some embodiments, mayreceive a request to read data from the cloud-based storage system 318and may ultimately send a request to read data to one or more of thecloud computing instances 340 a, 340 b, 340 n with local storage 330,334, 338.

When a request to write data is received by a particular cloud computinginstance 340 a, 340 b, 340 n with local storage 330, 334, 338, thesoftware daemon 328, 332, 336 may be configured to not only write thedata to its own local storage 330, 334, 338 resources and anyappropriate block storage 342, 344, 346 resources, but the softwaredaemon 328, 332, 336 may also be configured to write the data tocloud-based object storage 348 that is attached to the particular cloudcomputing instance 340 a, 340 b, 340 n. The cloud-based object storage348 that is attached to the particular cloud computing instance 340 a,340 b, 340 n may be embodied, for example, as Amazon Simple StorageService (‘S3’). In other embodiments, the cloud computing instances 320,322 that each include the storage controller application 324, 326 mayinitiate the storage of the data in the local storage 330, 334, 338 ofthe cloud computing instances 340 a, 340 b, 340 n and the cloud-basedobject storage 348. In other embodiments, rather than using both thecloud computing instances 340 a, 340 b, 340 n with local storage 330,334, 338 (also referred to herein as ‘virtual drives’) and thecloud-based object storage 348 to store data, a persistent storage layermay be implemented in other ways. For example, one or more Azure Ultradisks may be used to persistently store data (e.g., after the data hasbeen written to the NVRAM layer).

While the local storage 330, 334, 338 resources and the block storage342, 344, 346 resources that are utilized by the cloud computinginstances 340 a, 340 b, 340 n may support block-level access, thecloud-based object storage 348 that is attached to the particular cloudcomputing instance 340 a, 340 b, 340 n supports only object-basedaccess. The software daemon 328, 332, 336 may therefore be configured totake blocks of data, package those blocks into objects, and write theobjects to the cloud-based object storage 348 that is attached to theparticular cloud computing instance 340 a, 340 b, 340 n.

Consider an example in which data is written to the local storage 330,334, 338 resources and the block storage 342, 344, 346 resources thatare utilized by the cloud computing instances 340 a, 340 b, 340 n in 1MB blocks. In such an example, assume that a user of the cloud-basedstorage system 318 issues a request to write data that, after beingcompressed and deduplicated by the storage controller application 324,326 results in the need to write 5 MB of data. In such an example,writing the data to the local storage 330, 334, 338 resources and theblock storage 342, 344, 346 resources that are utilized by the cloudcomputing instances 340 a, 340 b, 340 n is relatively straightforward as5 blocks that are 1 MB in size are written to the local storage 330,334, 338 resources and the block storage 342, 344, 346 resources thatare utilized by the cloud computing instances 340 a, 340 b, 340 n. Insuch an example, the software daemon 328, 332, 336 may also beconfigured to create five objects containing distinct 1 MB chunks of thedata. As such, in some embodiments, each object that is written to thecloud-based object storage 348 may be identical (or nearly identical) insize. Readers will appreciate that in such an example, metadata that isassociated with the data itself may be included in each object (e.g.,the first 1 MB of the object is data and the remaining portion ismetadata associated with the data). Readers will appreciate that thecloud-based object storage 348 may be incorporated into the cloud-basedstorage system 318 to increase the durability of the cloud-based storagesystem 318.

In some embodiments, all data that is stored by the cloud-based storagesystem 318 may be stored in both: 1) the cloud-based object storage 348,and 2) at least one of the local storage 330, 334, 338 resources orblock storage 342, 344, 346 resources that are utilized by the cloudcomputing instances 340 a, 340 b, 340 n. In such embodiments, the localstorage 330, 334, 338 resources and block storage 342, 344, 346resources that are utilized by the cloud computing instances 340 a, 340b, 340 n may effectively operate as cache that generally includes alldata that is also stored in S3, such that all reads of data may beserviced by the cloud computing instances 340 a, 340 b, 340 n withoutrequiring the cloud computing instances 340 a, 340 b, 340 n to accessthe cloud-based object storage 348. Readers will appreciate that inother embodiments, however, all data that is stored by the cloud-basedstorage system 318 may be stored in the cloud-based object storage 348,but less than all data that is stored by the cloud-based storage system318 may be stored in at least one of the local storage 330, 334, 338resources or block storage 342, 344, 346 resources that are utilized bythe cloud computing instances 340 a, 340 b, 340 n. In such an example,various policies may be utilized to determine which subset of the datathat is stored by the cloud-based storage system 318 should reside inboth: 1) the cloud-based object storage 348, and 2) at least one of thelocal storage 330, 334, 338 resources or block storage 342, 344, 346resources that are utilized by the cloud computing instances 340 a, 340b, 340 n.

One or more modules of computer program instructions that are executingwithin the cloud-based storage system 318 (e.g., a monitoring modulethat is executing on its own EC2 instance) may be designed to handle thefailure of one or more of the cloud computing instances 340 a, 340 b,340 n with local storage 330, 334, 338. In such an example, themonitoring module may handle the failure of one or more of the cloudcomputing instances 340 a, 340 b, 340 n with local storage 330, 334, 338by creating one or more new cloud computing instances with localstorage, retrieving data that was stored on the failed cloud computinginstances 340 a, 340 b, 340 n from the cloud-based object storage 348,and storing the data retrieved from the cloud-based object storage 348in local storage on the newly created cloud computing instances. Readerswill appreciate that many variants of this process may be implemented.

Readers will appreciate that various performance aspects of thecloud-based storage system 318 may be monitored (e.g., by a monitoringmodule that is executing in an EC2 instance) such that the cloud-basedstorage system 318 can be scaled-up or scaled-out as needed. Forexample, if the cloud computing instances 320, 322 that are used tosupport the execution of a storage controller application 324, 326 areundersized and not sufficiently servicing the I/O requests that areissued by users of the cloud-based storage system 318, a monitoringmodule may create a new, more powerful cloud computing instance (e.g., acloud computing instance of a type that includes more processing power,more memory, etc. . . . ) that includes the storage controllerapplication such that the new, more powerful cloud computing instancecan begin operating as the primary controller. Likewise, if themonitoring module determines that the cloud computing instances 320, 322that are used to support the execution of a storage controllerapplication 324, 326 are oversized and that cost savings could be gainedby switching to a smaller, less powerful cloud computing instance, themonitoring module may create a new, less powerful (and less expensive)cloud computing instance that includes the storage controllerapplication such that the new, less powerful cloud computing instancecan begin operating as the primary controller.

The storage systems described above may carry out intelligent databackup techniques through which data stored in the storage system may becopied and stored in a distinct location to avoid data loss in the eventof equipment failure or some other form of catastrophe. For example, thestorage systems described above may be configured to examine each backupto avoid restoring the storage system to an undesirable state. Consideran example in which malware infects the storage system. In such anexample, the storage system may include software resources 314 that canscan each backup to identify backups that were captured before themalware infected the storage system and those backups that were capturedafter the malware infected the storage system. In such an example, thestorage system may restore itself from a backup that does not includethe malware—or at least not restore the portions of a backup thatcontained the malware. In such an example, the storage system mayinclude software resources 314 that can scan each backup to identify thepresences of malware (or a virus, or some other undesirable), forexample, by identifying write operations that were serviced by thestorage system and originated from a network subnet that is suspected tohave delivered the malware, by identifying write operations that wereserviced by the storage system and originated from a user that issuspected to have delivered the malware, by identifying write operationsthat were serviced by the storage system and examining the content ofthe write operation against fingerprints of the malware, and in manyother ways.

Readers will further appreciate that the backups (often in the form ofone or more snapshots) may also be utilized to perform rapid recovery ofthe storage system. Consider an example in which the storage system isinfected with ransomware that locks users out of the storage system. Insuch an example, software resources 314 within the storage system may beconfigured to detect the presence of ransomware and may be furtherconfigured to restore the storage system to a point-in-time, using theretained backups, prior to the point-in-time at which the ransomwareinfected the storage system. In such an example, the presence ofransomware may be explicitly detected through the use of software toolsutilized by the system, through the use of a key (e.g., a USB drive)that is inserted into the storage system, or in a similar way. Likewise,the presence of ransomware may be inferred in response to systemactivity meeting a predetermined fingerprint such as, for example, noreads or writes coming into the system for a predetermined period oftime.

Readers will appreciate that the various components described above maybe grouped into one or more optimized computing packages as convergedinfrastructures. Such converged infrastructures may include pools ofcomputers, storage and networking resources that can be shared bymultiple applications and managed in a collective manner usingpolicy-driven processes. Such converged infrastructures may beimplemented with a converged infrastructure reference architecture, withstandalone appliances, with a software driven hyper-converged approach(e.g., hyper-converged infrastructures), or in other ways.

Readers will appreciate that the storage systems described in thisdisclosure may be useful for supporting various types of softwareapplications. In fact, the storage systems may be ‘application aware’ inthe sense that the storage systems may obtain, maintain, or otherwisehave access to information describing connected applications (e.g.,applications that utilize the storage systems) to optimize the operationof the storage system based on intelligence about the applications andtheir utilization patterns. For example, the storage system may optimizedata layouts, optimize caching behaviors, optimize ‘QoS’ levels, orperform some other optimization that is designed to improve the storageperformance that is experienced by the application.

As an example of one type of application that may be supported by thestorage systems describe herein, the storage system 306 may be useful insupporting artificial intelligence (‘AI’) applications, databaseapplications, XOps projects (e.g., DevOps projects, DataOps projects,MLOps projects, ModelOps projects, PlatformOps projects), electronicdesign automation tools, event-driven software applications, highperformance computing applications, simulation applications, high-speeddata capture and analysis applications, machine learning applications,media production applications, media serving applications, picturearchiving and communication systems (‘PACS’) applications, softwaredevelopment applications, virtual reality applications, augmentedreality applications, and many other types of applications by providingstorage resources to such applications.

In view of the fact that the storage systems include compute resources,storage resources, and a wide variety of other resources, the storagesystems may be well suited to support applications that are resourceintensive such as, for example, AI applications. AI applications may bedeployed in a variety of fields, including: predictive maintenance inmanufacturing and related fields, healthcare applications such aspatient data & risk analytics, retail and marketing deployments (e.g.,search advertising, social media advertising), supply chains solutions,fintech solutions such as business analytics & reporting tools,operational deployments such as real-time analytics tools, applicationperformance management tools, IT infrastructure management tools, andmany others.

Such AI applications may enable devices to perceive their environmentand take actions that maximize their chance of success at some goal.Examples of such AI applications can include IBM Watson™, MicrosoftOxford™, Google DeepMind™, Baidu Minwa™, and others.

The storage systems described above may also be well suited to supportother types of applications that are resource intensive such as, forexample, machine learning applications. Machine learning applicationsmay perform various types of data analysis to automate analytical modelbuilding. Using algorithms that iteratively learn from data, machinelearning applications can enable computers to learn without beingexplicitly programmed. One particular area of machine learning isreferred to as reinforcement learning, which involves taking suitableactions to maximize reward in a particular situation.

In addition to the resources already described, the storage systemsdescribed above may also include graphics processing units (‘GPUs’),occasionally referred to as visual processing unit (‘VPUs’). Such GPUsmay be embodied as specialized electronic circuits that rapidlymanipulate and alter memory to accelerate the creation of images in aframe buffer intended for output to a display device. Such GPUs may beincluded within any of the computing devices that are part of thestorage systems described above, including as one of many individuallyscalable components of a storage system, where other examples ofindividually scalable components of such storage system can includestorage components, memory components, compute components (e.g., CPUs,FPGAs, ASICs), networking components, software components, and others.In addition to GPUs, the storage systems described above may alsoinclude neural network processors (‘NNPs’) for use in various aspects ofneural network processing. Such NNPs may be used in place of (or inaddition to) GPUs and may also be independently scalable.

As described above, the storage systems described herein may beconfigured to support artificial intelligence applications, machinelearning applications, big data analytics applications, and many othertypes of applications. The rapid growth in these sort of applications isbeing driven by three technologies: deep learning (DL), GPU processors,and Big Data. Deep learning is a computing model that makes use ofmassively parallel neural networks inspired by the human brain. Insteadof experts handcrafting software, a deep learning model writes its ownsoftware by learning from lots of examples. Such GPUs may includethousands of cores that are well-suited to run algorithms that looselyrepresent the parallel nature of the human brain.

Advances in deep neural networks, including the development ofmulti-layer neural networks, have ignited a new wave of algorithms andtools for data scientists to tap into their data with artificialintelligence (AI). With improved algorithms, larger data sets, andvarious frameworks (including open-source software libraries for machinelearning across a range of tasks), data scientists are tackling new usecases like autonomous driving vehicles, natural language processing andunderstanding, computer vision, machine reasoning, strong AI, and manyothers. Applications of such techniques may include: machine andvehicular object detection, identification and avoidance; visualrecognition, classification and tagging; algorithmic financial tradingstrategy performance management; simultaneous localization and mapping;predictive maintenance of high-value machinery; prevention against cybersecurity threats, expertise automation; image recognition andclassification; question answering; robotics; text analytics(extraction, classification) and text generation and translation; andmany others. Applications of AI techniques has materialized in a widearray of products include, for example, Amazon Echo's speech recognitiontechnology that allows users to talk to their machines, GoogleTranslate™ which allows for machine-based language translation,Spotify's Discover Weekly that provides recommendations on new songs andartists that a user may like based on the user's usage and trafficanalysis, Quill's text generation offering that takes structured dataand turns it into narrative stories, Chatbots that provide real-time,contextually specific answers to questions in a dialog format, and manyothers.

Data is the heart of modern AI and deep learning algorithms. Beforetraining can begin, one problem that must be addressed revolves aroundcollecting the labeled data that is crucial for training an accurate AImodel. A full scale AI deployment may be required to continuouslycollect, clean, transform, label, and store large amounts of data.Adding additional high quality data points directly translates to moreaccurate models and better insights. Data samples may undergo a seriesof processing steps including, but not limited to: 1) ingesting the datafrom an external source into the training system and storing the data inraw form, 2) cleaning and transforming the data in a format convenientfor training, including linking data samples to the appropriate label,3) exploring parameters and models, quickly testing with a smallerdataset, and iterating to converge on the most promising models to pushinto the production cluster, 4) executing training phases to selectrandom batches of input data, including both new and older samples, andfeeding those into production GPU servers for computation to updatemodel parameters, and 5) evaluating including using a holdback portionof the data not used in training in order to evaluate model accuracy onthe holdout data. This lifecycle may apply for any type of parallelizedmachine learning, not just neural networks or deep learning. Forexample, standard machine learning frameworks may rely on CPUs insteadof GPUs but the data ingest and training workflows may be the same.Readers will appreciate that a single shared storage data hub creates acoordination point throughout the lifecycle without the need for extradata copies among the ingest, preprocessing, and training stages. Rarelyis the ingested data used for only one purpose, and shared storage givesthe flexibility to train multiple different models or apply traditionalanalytics to the data.

Readers will appreciate that each stage in the AI data pipeline may havevarying requirements from the data hub (e.g., the storage system orcollection of storage systems). Scale-out storage systems must deliveruncompromising performance for all manner of access types andpatterns—from small, metadata-heavy to large files, from random tosequential access patterns, and from low to high concurrency. Thestorage systems described above may serve as an ideal AI data hub as thesystems may service unstructured workloads. In the first stage, data isideally ingested and stored on to the same data hub that followingstages will use, in order to avoid excess data copying. The next twosteps can be done on a standard compute server that optionally includesa GPU, and then in the fourth and last stage, full training productionjobs are run on powerful GPU-accelerated servers. Often, there is aproduction pipeline alongside an experimental pipeline operating on thesame dataset. Further, the GPU-accelerated servers can be usedindependently for different models or joined together to train on onelarger model, even spanning multiple systems for distributed training.If the shared storage tier is slow, then data must be copied to localstorage for each phase, resulting in wasted time staging data ontodifferent servers. The ideal data hub for the AI training pipelinedelivers performance similar to data stored locally on the server nodewhile also having the simplicity and performance to enable all pipelinestages to operate concurrently.

In order for the storage systems described above to serve as a data hubor as part of an AI deployment, in some embodiments the storage systemsmay be configured to provide DMA between storage devices that areincluded in the storage systems and one or more GPUs that are used in anAI or big data analytics pipeline. The one or more GPUs may be coupledto the storage system, for example, via NVMe-over-Fabrics (‘NVMe-oF’)such that bottlenecks such as the host CPU can be bypassed and thestorage system (or one of the components contained therein) can directlyaccess GPU memory. In such an example, the storage systems may leverageAPI hooks to the GPUs to transfer data directly to the GPUs. Forexample, the GPUs may be embodied as Nvidia™ GPUs and the storagesystems may support GPUDirect Storage (‘GDS’) software, or have similarproprietary software, that enables the storage system to transfer datato the GPUs via RDMA or similar mechanism.

Although the preceding paragraphs discuss deep learning applications,readers will appreciate that the storage systems described herein mayalso be part of a distributed deep learning (‘DDL’) platform to supportthe execution of DDL algorithms. The storage systems described above mayalso be paired with other technologies such as TensorFlow, anopen-source software library for dataflow programming across a range oftasks that may be used for machine learning applications such as neuralnetworks, to facilitate the development of such machine learning models,applications, and so on.

The storage systems described above may also be used in a neuromorphiccomputing environment. Neuromorphic computing is a form of computingthat mimics brain cells. To support neuromorphic computing, anarchitecture of interconnected “neurons” replace traditional computingmodels with low-powered signals that go directly between neurons formore efficient computation. Neuromorphic computing may make use ofvery-large-scale integration (VLSI) systems containing electronic analogcircuits to mimic neuro-biological architectures present in the nervoussystem, as well as analog, digital, mixed-mode analog/digital VLSI, andsoftware systems that implement models of neural systems for perception,motor control, or multisensory integration.

Readers will appreciate that the storage systems described above may beconfigured to support the storage or use of (among other types of data)blockchains and derivative items such as, for example, open sourceblockchains and related tools that are part of the IBM™ Hyperledgerproject, permissioned blockchains in which a certain number of trustedparties are allowed to access the block chain, blockchain products thatenable developers to build their own distributed ledger projects, andothers. Blockchains and the storage systems described herein may beleveraged to support on-chain storage of data as well as off-chainstorage of data.

Off-chain storage of data can be implemented in a variety of ways andcan occur when the data itself is not stored within the blockchain. Forexample, in one embodiment, a hash function may be utilized and the dataitself may be fed into the hash function to generate a hash value. Insuch an example, the hashes of large pieces of data may be embeddedwithin transactions, instead of the data itself. Readers will appreciatethat, in other embodiments, alternatives to blockchains may be used tofacilitate the decentralized storage of information. For example, onealternative to a blockchain that may be used is a blockweave. Whileconventional blockchains store every transaction to achieve validation,a blockweave permits secure decentralization without the usage of theentire chain, thereby enabling low cost on-chain storage of data. Suchblockweaves may utilize a consensus mechanism that is based on proof ofaccess (PoA) and proof of work (PoW).

The storage systems described above may, either alone or in combinationwith other computing devices, be used to support in-memory computingapplications. In-memory computing involves the storage of information inRAM that is distributed across a cluster of computers. Readers willappreciate that the storage systems described above, especially thosethat are configurable with customizable amounts of processing resources,storage resources, and memory resources (e.g., those systems in whichblades that contain configurable amounts of each type of resource), maybe configured in a way so as to provide an infrastructure that cansupport in-memory computing. Likewise, the storage systems describedabove may include component parts (e.g., NVDIMMs, 3D crosspoint storagethat provide fast random access memory that is persistent) that canactually provide for an improved in-memory computing environment ascompared to in-memory computing environments that rely on RAMdistributed across dedicated servers.

In some embodiments, the storage systems described above may beconfigured to operate as a hybrid in-memory computing environment thatincludes a universal interface to all storage media (e.g., RAM, flashstorage, 3D crosspoint storage). In such embodiments, users may have noknowledge regarding the details of where their data is stored but theycan still use the same full, unified API to address data. In suchembodiments, the storage system may (in the background) move data to thefastest layer available—including intelligently placing the data independence upon various characteristics of the data or in dependenceupon some other heuristic. In such an example, the storage systems mayeven make use of existing products such as Apache Ignite and GridGain tomove data between the various storage layers, or the storage systems maymake use of custom software to move data between the various storagelayers. The storage systems described herein may implement variousoptimizations to improve the performance of in-memory computing such as,for example, having computations occur as close to the data as possible.

Readers will further appreciate that in some embodiments, the storagesystems described above may be paired with other resources to supportthe applications described above. For example, one infrastructure couldinclude primary compute in the form of servers and workstations whichspecialize in using General-purpose computing on graphics processingunits (‘GPGPU’) to accelerate deep learning applications that areinterconnected into a computation engine to train parameters for deepneural networks. Each system may have Ethernet external connectivity,InfiniBand external connectivity, some other form of externalconnectivity, or some combination thereof. In such an example, the GPUscan be grouped for a single large training or used independently totrain multiple models. The infrastructure could also include a storagesystem such as those described above to provide, for example, ascale-out all-flash file or object store through which data can beaccessed via high-performance protocols such as NFS, S3, and so on. Theinfrastructure can also include, for example, redundant top-of-rackEthernet switches connected to storage and compute via ports in MLAGport channels for redundancy. The infrastructure could also includeadditional compute in the form of whitebox servers, optionally withGPUs, for data ingestion, pre-processing, and model debugging. Readerswill appreciate that additional infrastructures are also be possible.

Readers will appreciate that the storage systems described above, eitheralone or in coordination with other computing machinery may beconfigured to support other AI related tools. For example, the storagesystems may make use of tools like ONXX or other open neural networkexchange formats that make it easier to transfer models written indifferent AI frameworks. Likewise, the storage systems may be configuredto support tools like Amazon's Gluon that allow developers to prototype,build, and train deep learning models. In fact, the storage systemsdescribed above may be part of a larger platform, such as IBM™ CloudPrivate for Data, that includes integrated data science, dataengineering and application building services.

Readers will further appreciate that the storage systems described abovemay also be deployed as an edge solution. Such an edge solution may bein place to optimize cloud computing systems by performing dataprocessing at the edge of the network, near the source of the data. Edgecomputing can push applications, data and computing power (i.e.,services) away from centralized points to the logical extremes of anetwork. Through the use of edge solutions such as the storage systemsdescribed above, computational tasks may be performed using the computeresources provided by such storage systems, data may be storage usingthe storage resources of the storage system, and cloud-based servicesmay be accessed through the use of various resources of the storagesystem (including networking resources). By performing computationaltasks on the edge solution, storing data on the edge solution, andgenerally making use of the edge solution, the consumption of expensivecloud-based resources may be avoided and, in fact, performanceimprovements may be experienced relative to a heavier reliance oncloud-based resources.

While many tasks may benefit from the utilization of an edge solution,some particular uses may be especially suited for deployment in such anenvironment. For example, devices like drones, autonomous cars, robots,and others may require extremely rapid processing—so fast, in fact, thatsending data up to a cloud environment and back to receive dataprocessing support may simply be too slow. As an additional example,some IoT devices such as connected video cameras may not be well-suitedfor the utilization of cloud-based resources as it may be impractical(not only from a privacy perspective, security perspective, or afinancial perspective) to send the data to the cloud simply because ofthe pure volume of data that is involved. As such, many tasks thatreally on data processing, storage, or communications may be bettersuited by platforms that include edge solutions such as the storagesystems described above.

The storage systems described above may alone, or in combination withother computing resources, serves as a network edge platform thatcombines compute resources, storage resources, networking resources,cloud technologies and network virtualization technologies, and so on.As part of the network, the edge may take on characteristics similar toother network facilities, from the customer premise and backhaulaggregation facilities to Points of Presence (PoPs) and regional datacenters. Readers will appreciate that network workloads, such as VirtualNetwork Functions (VNFs) and others, will reside on the network edgeplatform. Enabled by a combination of containers and virtual machines,the network edge platform may rely on controllers and schedulers thatare no longer geographically co-located with the data processingresources. The functions, as microservices, may split into controlplanes, user and data planes, or even state machines, allowing forindependent optimization and scaling techniques to be applied. Such userand data planes may be enabled through increased accelerators, boththose residing in server platforms, such as FPGAs and Smart NICs, andthrough SDN-enabled merchant silicon and programmable ASICs.

The storage systems described above may also be optimized for use in bigdata analytics, including being leveraged as part of a composable dataanalytics pipeline where containerized analytics architectures, forexample, make analytics capabilities more composable. Big data analyticsmay be generally described as the process of examining large and varieddata sets to uncover hidden patterns, unknown correlations, markettrends, customer preferences and other useful information that can helporganizations make more-informed business decisions. As part of thatprocess, semi-structured and unstructured data such as, for example,internet clickstream data, web server logs, social media content, textfrom customer emails and survey responses, mobile-phone call-detailrecords, IoT sensor data, and other data may be converted to astructured form.

The storage systems described above may also support (includingimplementing as a system interface) applications that perform tasks inresponse to human speech. For example, the storage systems may supportthe execution intelligent personal assistant applications such as, forexample, Amazon's Alexa™, Apple Siri™, Google Voice™, Samsung Bixby™,Microsoft Cortana™, and others. While the examples described in theprevious sentence make use of voice as input, the storage systemsdescribed above may also support chatbots, talkbots, chatterbots, orartificial conversational entities or other applications that areconfigured to conduct a conversation via auditory or textual methods.Likewise, the storage system may actually execute such an application toenable a user such as a system administrator to interact with thestorage system via speech. Such applications are generally capable ofvoice interaction, music playback, making to-do lists, setting alarms,streaming podcasts, playing audiobooks, and providing weather, traffic,and other real time information, such as news, although in embodimentsin accordance with the present disclosure, such applications may beutilized as interfaces to various system management operations.

The storage systems described above may also implement AI platforms fordelivering on the vision of self-driving storage. Such AI platforms maybe configured to deliver global predictive intelligence by collectingand analyzing large amounts of storage system telemetry data points toenable effortless management, analytics and support. In fact, suchstorage systems may be capable of predicting both capacity andperformance, as well as generating intelligent advice on workloaddeployment, interaction and optimization. Such AI platforms may beconfigured to scan all incoming storage system telemetry data against alibrary of issue fingerprints to predict and resolve incidents inreal-time, before they impact customer environments, and captureshundreds of variables related to performance that are used to forecastperformance load.

The storage systems described above may support the serialized orsimultaneous execution of artificial intelligence applications, machinelearning applications, data analytics applications, datatransformations, and other tasks that collectively may form an AIladder. Such an AI ladder may effectively be formed by combining suchelements to form a complete data science pipeline, where existdependencies between elements of the AI ladder. For example, AI mayrequire that some form of machine learning has taken place, machinelearning may require that some form of analytics has taken place,analytics may require that some form of data and informationarchitecting has taken place, and so on. As such, each element may beviewed as a rung in an AI ladder that collectively can form a completeand sophisticated AI solution.

The storage systems described above may also, either alone or incombination with other computing environments, be used to deliver an AIeverywhere experience where AI permeates wide and expansive aspects ofbusiness and life. For example, AI may play an important role in thedelivery of deep learning solutions, deep reinforcement learningsolutions, artificial general intelligence solutions, autonomousvehicles, cognitive computing solutions, commercial UAVs or drones,conversational user interfaces, enterprise taxonomies, ontologymanagement solutions, machine learning solutions, smart dust, smartrobots, smart workplaces, and many others.

The storage systems described above may also, either alone or incombination with other computing environments, be used to deliver a widerange of transparently immersive experiences (including those that usedigital twins of various “things” such as people, places, processes,systems, and so on) where technology can introduce transparency betweenpeople, businesses, and things. Such transparently immersive experiencesmay be delivered as augmented reality technologies, connected homes,virtual reality technologies, brain-computer interfaces, humanaugmentation technologies, nanotube electronics, volumetric displays, 4Dprinting technologies, or others.

The storage systems described above may also, either alone or incombination with other computing environments, be used to support a widevariety of digital platforms. Such digital platforms can include, forexample, 5G wireless systems and platforms, digital twin platforms, edgecomputing platforms, IoT platforms, quantum computing platforms,serverless PaaS, software-defined security, neuromorphic computingplatforms, and so on.

The storage systems described above may also be part of a multi-cloudenvironment in which multiple cloud computing and storage services aredeployed in a single heterogeneous architecture. In order to facilitatethe operation of such a multi-cloud environment, DevOps tools may bedeployed to enable orchestration across clouds. Likewise, continuousdevelopment and continuous integration tools may be deployed tostandardize processes around continuous integration and delivery, newfeature rollout and provisioning cloud workloads. By standardizing theseprocesses, a multi-cloud strategy may be implemented that enables theutilization of the best provider for each workload.

The storage systems described above may be used as a part of a platformto enable the use of crypto-anchors that may be used to authenticate aproduct's origins and contents to ensure that it matches a blockchainrecord associated with the product. Similarly, as part of a suite oftools to secure data stored on the storage system, the storage systemsdescribed above may implement various encryption technologies andschemes, including lattice cryptography. Lattice cryptography caninvolve constructions of cryptographic primitives that involve lattices,either in the construction itself or in the security proof. Unlikepublic-key schemes such as the RSA, Diffie-Hellman or Elliptic-Curvecryptosystems, which are easily attacked by a quantum computer, somelattice-based constructions appear to be resistant to attack by bothclassical and quantum computers.

A quantum computer is a device that performs quantum computing. Quantumcomputing is computing using quantum-mechanical phenomena, such assuperposition and entanglement. Quantum computers differ fromtraditional computers that are based on transistors, as such traditionalcomputers require that data be encoded into binary digits (bits), eachof which is always in one of two definite states (0 or 1). In contrastto traditional computers, quantum computers use quantum bits, which canbe in superpositions of states. A quantum computer maintains a sequenceof qubits, where a single qubit can represent a one, a zero, or anyquantum superposition of those two qubit states. A pair of qubits can bein any quantum superposition of 4 states, and three qubits in anysuperposition of 8 states. A quantum computer with n qubits cangenerally be in an arbitrary superposition of up to 2{circumflex over( )}n different states simultaneously, whereas a traditional computercan only be in one of these states at any one time. A quantum Turingmachine is a theoretical model of such a computer.

The storage systems described above may also be paired withFPGA-accelerated servers as part of a larger AI or ML infrastructure.Such FPGA-accelerated servers may reside near (e.g., in the same datacenter) the storage systems described above or even incorporated into anappliance that includes one or more storage systems, one or moreFPGA-accelerated servers, networking infrastructure that supportscommunications between the one or more storage systems and the one ormore FPGA-accelerated servers, as well as other hardware and softwarecomponents. Alternatively, FPGA-accelerated servers may reside within acloud computing environment that may be used to perform compute-relatedtasks for AI and ML jobs. Any of the embodiments described above may beused to collectively serve as a FPGA-based AI or ML platform. Readerswill appreciate that, in some embodiments of the FPGA-based AI or MLplatform, the FPGAs that are contained within the FPGA-acceleratedservers may be reconfigured for different types of ML models (e.g.,LSTMs, CNNs, GRUs). The ability to reconfigure the FPGAs that arecontained within the FPGA-accelerated servers may enable theacceleration of a ML or AI application based on the most optimalnumerical precision and memory model being used. Readers will appreciatethat by treating the collection of FPGA-accelerated servers as a pool ofFPGAs, any CPU in the data center may utilize the pool of FPGAs as ashared hardware microservice, rather than limiting a server to dedicatedaccelerators plugged into it.

The FPGA-accelerated servers and the GPU-accelerated servers describedabove may implement a model of computing where, rather than keeping asmall amount of data in a CPU and running a long stream of instructionsover it as occurred in more traditional computing models, the machinelearning model and parameters are pinned into the high-bandwidth on-chipmemory with lots of data streaming though the high-bandwidth on-chipmemory. FPGAs may even be more efficient than GPUs for this computingmodel, as the FPGAs can be programmed with only the instructions neededto run this kind of computing model.

The storage systems described above may be configured to provideparallel storage, for example, through the use of a parallel file systemsuch as BeeGFS. Such parallel files systems may include a distributedmetadata architecture. For example, the parallel file system may includea plurality of metadata servers across which metadata is distributed, aswell as components that include services for clients and storageservers.

The systems described above can support the execution of a wide array ofsoftware applications. Such software applications can be deployed in avariety of ways, including container-based deployment models.Containerized applications may be managed using a variety of tools. Forexample, containerized applications may be managed using Docker Swarm,Kubernetes, and others. Containerized applications may be used tofacilitate a serverless, cloud native computing deployment andmanagement model for software applications. In support of a serverless,cloud native computing deployment and management model for softwareapplications, containers may be used as part of an event handlingmechanisms (e.g., AWS Lambdas) such that various events cause acontainerized application to be spun up to operate as an event handler.

The systems described above may be deployed in a variety of ways,including being deployed in ways that support fifth generation (‘5G’)networks. 5G networks may support substantially faster datacommunications than previous generations of mobile communicationsnetworks and, as a consequence may lead to the disaggregation of dataand computing resources as modern massive data centers may become lessprominent and may be replaced, for example, by more-local, micro datacenters that are close to the mobile-network towers. The systemsdescribed above may be included in such local, micro data centers andmay be part of or paired to multi-access edge computing (‘MEC’) systems.Such MEC systems may enable cloud computing capabilities and an ITservice environment at the edge of the cellular network. By runningapplications and performing related processing tasks closer to thecellular customer, network congestion may be reduced and applicationsmay perform better.

The storage systems described above may also be configured to implementNVMe Zoned Namespaces. Through the use of NVMe Zoned Namespaces, thelogical address space of a namespace is divided into zones. Each zoneprovides a logical block address range that must be written sequentiallyand explicitly reset before rewriting, thereby enabling the creation ofnamespaces that expose the natural boundaries of the device and offloadmanagement of internal mapping tables to the host. In order to implementNVMe Zoned Name Spaces (‘ZNS’), ZNS SSDs or some other form of zonedblock devices may be utilized that expose a namespace logical addressspace using zones. With the zones aligned to the internal physicalproperties of the device, several inefficiencies in the placement ofdata can be eliminated. In such embodiments, each zone may be mapped,for example, to a separate application such that functions like wearlevelling and garbage collection could be performed on a per-zone orper-application basis rather than across the entire device. In order tosupport ZNS, the storage controllers described herein may be configuredwith to interact with zoned block devices through the usage of, forexample, the Linux™ kernel zoned block device interface or other tools.

The storage systems described above may also be configured to implementzoned storage in other ways such as, for example, through the usage ofshingled magnetic recording (SMR) storage devices. In examples wherezoned storage is used, device-managed embodiments may be deployed wherethe storage devices hide this complexity by managing it in the firmware,presenting an interface like any other storage device. Alternatively,zoned storage may be implemented via a host-managed embodiment thatdepends on the operating system to know how to handle the drive, andonly write sequentially to certain regions of the drive. Zoned storagemay similarly be implemented using a host-aware embodiment in which acombination of a drive managed and host managed implementation isdeployed.

The storage systems described herein may be used to form a data lake. Adata lake may operate as the first place that an organization's dataflows to, where such data may be in a raw format. Metadata tagging maybe implemented to facilitate searches of data elements in the data lake,especially in embodiments where the data lake contains multiple storesof data, in formats not easily accessible or readable (e.g.,unstructured data, semi-structured data, structured data). From the datalake, data may go downstream to a data warehouse where data may bestored in a more processed, packaged, and consumable format. The storagesystems described above may also be used to implement such a datawarehouse. In addition, a data mart or data hub may allow for data thatis even more easily consumed, where the storage systems described abovemay also be used to provide the underlying storage resources necessaryfor a data mart or data hub. In embodiments, queries the data lake mayrequire a schema-on-read approach, where data is applied to a plan orschema as it is pulled out of a stored location, rather than as it goesinto the stored location.

The storage systems described herein may also be configured implement arecovery point objective (‘RPO’), which may be establish by a user,established by an administrator, established as a system default,established as part of a storage class or service that the storagesystem is participating in the delivery of, or in some other way. A“recovery point objective” is a goal for the maximum time differencebetween the last update to a source dataset and the last recoverablereplicated dataset update that would be correctly recoverable, given areason to do so, from a continuously or frequently updated copy of thesource dataset. An update is correctly recoverable if it properly takesinto account all updates that were processed on the source dataset priorto the last recoverable replicated dataset update.

In synchronous replication, the RPO would be zero, meaning that undernormal operation, all completed updates on the source dataset should bepresent and correctly recoverable on the copy dataset. In best effortnearly synchronous replication, the RPO can be as low as a few seconds.In snapshot-based replication, the RPO can be roughly calculated as theinterval between snapshots plus the time to transfer the modificationsbetween a previous already transferred snapshot and the most recentto-be-replicated snapshot.

If updates accumulate faster than they are replicated, then an RPO canbe missed. If more data to be replicated accumulates between twosnapshots, for snapshot-based replication, than can be replicatedbetween taking the snapshot and replicating that snapshot's cumulativeupdates to the copy, then the RPO can be missed. If, again insnapshot-based replication, data to be replicated accumulates at afaster rate than could be transferred in the time between subsequentsnapshots, then replication can start to fall further behind which canextend the miss between the expected recovery point objective and theactual recovery point that is represented by the last correctlyreplicated update.

The storage systems described above may also be part of a shared nothingstorage cluster. In a shared nothing storage cluster, each node of thecluster has local storage and communicates with other nodes in thecluster through networks, where the storage used by the cluster is (ingeneral) provided only by the storage connected to each individual node.A collection of nodes that are synchronously replicating a dataset maybe one example of a shared nothing storage cluster, as each storagesystem has local storage and communicates to other storage systemsthrough a network, where those storage systems do not (in general) usestorage from somewhere else that they share access to through some kindof interconnect. In contrast, some of the storage systems describedabove are themselves built as a shared-storage cluster, since there aredrive shelves that are shared by the paired controllers. Other storagesystems described above, however, are built as a shared nothing storagecluster, as all storage is local to a particular node (e.g., a blade)and all communication is through networks that link the compute nodestogether.

In other embodiments, other forms of a shared nothing storage clustercan include embodiments where any node in the cluster has a local copyof all storage they need, and where data is mirrored through asynchronous style of replication to other nodes in the cluster either toensure that the data isn't lost or because other nodes are also usingthat storage. In such an embodiment, if a new cluster node needs somedata, that data can be copied to the new node from other nodes that havecopies of the data.

In some embodiments, mirror-copy-based shared storage clusters may storemultiple copies of all the cluster's stored data, with each subset ofdata replicated to a particular set of nodes, and different subsets ofdata replicated to different sets of nodes. In some variations,embodiments may store all of the cluster's stored data in all nodes,whereas in other variations nodes may be divided up such that a firstset of nodes will all store the same set of data and a second, differentset of nodes will all store a different set of data.

Readers will appreciate that RAFT-based databases (e.g., etcd) mayoperate like shared-nothing storage clusters where all RAFT nodes storeall data. The amount of data stored in a RAFT cluster, however, may belimited so that extra copies don't consume too much storage. A containerserver cluster might also be able to replicate all data to all clusternodes, presuming the containers don't tend to be too large and theirbulk data (the data manipulated by the applications that run in thecontainers) is stored elsewhere such as in an S3 cluster or an externalfile server. In such an example, the container storage may be providedby the cluster directly through its shared-nothing storage model, withthose containers providing the images that form the executionenvironment for parts of an application or service.

For further explanation, FIG. 3D illustrates an exemplary computingdevice 350 that may be specifically configured to perform one or more ofthe processes described herein. As shown in FIG. 3D, computing device350 may include a communication interface 352, a processor 354, astorage device 356, and an input/output (“I/O”) module 358communicatively connected one to another via a communicationinfrastructure 360. While an exemplary computing device 350 is shown inFIG. 3D, the components illustrated in FIG. 3D are not intended to belimiting. Additional or alternative components may be used in otherembodiments. Components of computing device 350 shown in FIG. 3D willnow be described in additional detail.

Communication interface 352 may be configured to communicate with one ormore computing devices. Examples of communication interface 352 include,without limitation, a wired network interface (such as a networkinterface card), a wireless network interface (such as a wirelessnetwork interface card), a modem, an audio/video connection, and anyother suitable interface.

Processor 354 generally represents any type or form of processing unitcapable of processing data and/or interpreting, executing, and/ordirecting execution of one or more of the instructions, processes,and/or operations described herein. Processor 354 may perform operationsby executing computer-executable instructions 362 (e.g., an application,software, code, and/or other executable data instance) stored in storagedevice 356.

Storage device 356 may include one or more data storage media, devices,or configurations and may employ any type, form, and combination of datastorage media and/or device. For example, storage device 356 mayinclude, but is not limited to, any combination of the non-volatilemedia and/or volatile media described herein. Electronic data, includingdata described herein, may be temporarily and/or permanently stored instorage device 356. For example, data representative ofcomputer-executable instructions 362 configured to direct processor 354to perform any of the operations described herein may be stored withinstorage device 356. In some examples, data may be arranged in one ormore databases residing within storage device 356.

I/O module 358 may include one or more I/O modules configured to receiveuser input and provide user output. I/O module 358 may include anyhardware, firmware, software, or combination thereof supportive of inputand output capabilities. For example, I/O module 358 may includehardware and/or software for capturing user input, including, but notlimited to, a keyboard or keypad, a touchscreen component (e.g.,touchscreen display), a receiver (e.g., an RF or infrared receiver),motion sensors, and/or one or more input buttons.

I/O module 358 may include one or more devices for presenting output toa user, including, but not limited to, a graphics engine, a display(e.g., a display screen), one or more output drivers (e.g., displaydrivers), one or more audio speakers, and one or more audio drivers. Incertain embodiments, I/O module 358 is configured to provide graphicaldata to a display for presentation to a user. The graphical data may berepresentative of one or more graphical user interfaces and/or any othergraphical content as may serve a particular implementation. In someexamples, any of the systems, computing devices, and/or other componentsdescribed herein may be implemented by computing device 350.

For further explanation, FIG. 3E illustrates an example of a fleet ofstorage systems 376 for providing storage services (also referred toherein as ‘data services’). The fleet of storage systems 376 depicted inFIG. 3 includes a plurality of storage systems 374 a, 374 b, 374 c, 374d, 374 n that may each be similar to the storage systems describedherein. The storage systems 374 a, 374 b, 374 c, 374 d, 374 n in thefleet of storage systems 376 may be embodied as identical storagesystems or as different types of storage systems. For example, two ofthe storage systems 374 a, 374 n depicted in FIG. 3E are depicted asbeing cloud-based storage systems, as the resources that collectivelyform each of the storage systems 374 a, 374 n are provided by distinctcloud services providers 370, 372. For example, the first cloud servicesprovider 370 may be Amazon AWS™ whereas the second cloud servicesprovider 372 is Microsoft Azure™, although in other embodiments one ormore public clouds, private clouds, or combinations thereof may be usedto provide the underlying resources that are used to form a particularstorage system in the fleet of storage systems 376.

The example depicted in FIG. 3E includes an edge management service 382for delivering storage services in accordance with some embodiments ofthe present disclosure. The storage services (also referred to herein as‘data services’) that are delivered may include, for example, servicesto provide a certain amount of storage to a consumer, services toprovide storage to a consumer in accordance with a predetermined servicelevel agreement, services to provide storage to a consumer in accordancewith predetermined regulatory requirements, and many others.

The edge management service 382 depicted in FIG. 3E may be embodied, forexample, as one or more modules of computer program instructionsexecuting on computer hardware such as one or more computer processors.Alternatively, the edge management service 382 may be embodied as one ormore modules of computer program instructions executing on a virtualizedexecution environment such as one or more virtual machines, in one ormore containers, or in some other way. In other embodiments, the edgemanagement service 382 may be embodied as a combination of theembodiments described above, including embodiments where the one or moremodules of computer program instructions that are included in the edgemanagement service 382 are distributed across multiple physical orvirtual execution environments.

The edge management service 382 may operate as a gateway for providingstorage services to storage consumers, where the storage servicesleverage storage offered by one or more storage systems 374 a, 374 b,374 c, 374 d, 374 n. For example, the edge management service 382 may beconfigured to provide storage services to host devices 378 a, 378 b, 378c, 378 d, 378 n that are executing one or more applications that consumethe storage services. In such an example, the edge management service382 may operate as a gateway between the host devices 378 a, 378 b, 378c, 378 d, 378 n and the storage systems 374 a, 374 b, 374 c, 374 d, 374n, rather than requiring that the host devices 378 a, 378 b, 378 c, 378d, 378 n directly access the storage systems 374 a, 374 b, 374 c, 374 d,374 n.

The edge management service 382 of FIG. 3E exposes a storage servicesmodule 380 to the host devices 378 a, 378 b, 378 c, 378 d, 378 n of FIG.3E, although in other embodiments the edge management service 382 mayexpose the storage services module 380 to other consumers of the variousstorage services. The various storage services may be presented toconsumers via one or more user interfaces, via one or more APIs, orthrough some other mechanism provided by the storage services module380. As such, the storage services module 380 depicted in FIG. 3E may beembodied as one or more modules of computer program instructionsexecuting on physical hardware, on a virtualized execution environment,or combinations thereof, where executing such modules causes enables aconsumer of storage services to be offered, select, and access thevarious storage services.

The edge management service 382 of FIG. 3E also includes a systemmanagement services module 384. The system management services module384 of FIG. 3E includes one or more modules of computer programinstructions that, when executed, perform various operations incoordination with the storage systems 374 a, 374 b, 374 c, 374 d, 374 nto provide storage services to the host devices 378 a, 378 b, 378 c, 378d, 378 n. The system management services module 384 may be configured,for example, to perform tasks such as provisioning storage resourcesfrom the storage systems 374 a, 374 b, 374 c, 374 d, 374 n via one ormore APIs exposed by the storage systems 374 a, 374 b, 374 c, 374 d, 374n, migrating datasets or workloads amongst the storage systems 374 a,374 b, 374 c, 374 d, 374 n via one or more APIs exposed by the storagesystems 374 a, 374 b, 374 c, 374 d, 374 n, setting one or more tunableparameters (i.e., one or more configurable settings) on the storagesystems 374 a, 374 b, 374 c, 374 d, 374 n via one or more APIs exposedby the storage systems 374 a, 374 b, 374 c, 374 d, 374 n, and so on. Forexample, many of the services described below relate to embodimentswhere the storage systems 374 a, 374 b, 374 c, 374 d, 374 n areconfigured to operate in some way. In such examples, the systemmanagement services module 384 may be responsible for using APIs (orsome other mechanism) provided by the storage systems 374 a, 374 b, 374c, 374 d, 374 n to configure the storage systems 374 a, 374 b, 374 c,374 d, 374 n to operate in the ways described below.

In addition to configuring the storage systems 374 a, 374 b, 374 c, 374d, 374 n, the edge management service 382 itself may be configured toperform various tasks required to provide the various storage services.Consider an example in which the storage service includes a servicethat, when selected and applied, causes personally identifiableinformation (PIP) contained in a dataset to be obfuscated when thedataset is accessed. In such an example, the storage systems 374 a, 374b, 374 c, 374 d, 374 n may be configured to obfuscate PII when servicingread requests directed to the dataset. Alternatively, the storagesystems 374 a, 374 b, 374 c, 374 d, 374 n may service reads by returningdata that includes the PII, but the edge management service 382 itselfmay obfuscate the PII as the data is passed through the edge managementservice 382 on its way from the storage systems 374 a, 374 b, 374 c, 374d, 374 n to the host devices 378 a, 378 b, 378 c, 378 d, 378 n.

The storage systems 374 a, 374 b, 374 c, 374 d, 374 n depicted in FIG.3E may be embodied as one or more of the storage systems described abovewith reference to FIGS. 1A-3D, including variations thereof. In fact,the storage systems 374 a, 374 b, 374 c, 374 d, 374 n may serve as apool of storage resources where the individual components in that poolhave different performance characteristics, different storagecharacteristics, and so on. For example, one of the storage systems 374a may be a cloud-based storage system, another storage system 374 b maybe a storage system that provides block storage, another storage system374 c may be a storage system that provides file storage, anotherstorage system 374 d may be a relatively high-performance storage systemwhile another storage system 374 n may be a relatively low-performancestorage system, and so on. In alternative embodiments, only a singlestorage system may be present.

The storage systems 374 a, 374 b, 374 c, 374 d, 374 n depicted in FIG.3E may also be organized into different failure domains so that thefailure of one storage system 374 a should be totally unrelated to thefailure of another storage system 374 b. For example, each of thestorage systems may receive power from independent power systems, eachof the storage systems may be coupled for data communications overindependent data communications networks, and so on. Furthermore, thestorage systems in a first failure domain may be accessed via a firstgateway whereas storage systems in a second failure domain may beaccessed via a second gateway. For example, the first gateway may be afirst instance of the edge management service 382 and the second gatewaymay be a second instance of the edge management service 382, includingembodiments where each instance is distinct, or each instance is part ofa distributed edge management service 382.

As an illustrative example of available storage services, storageservices may be presented to a user that are associated with differentlevels of data protection. For example, storage services may bepresented to the user that, when selected and enforced, guarantee theuser that data associated with that user will be protected such thatvarious recovery point objectives (‘RPO’) can be guaranteed. A firstavailable storage service may ensure, for example, that some datasetassociated with the user will be protected such that any data that ismore than 5 seconds old can be recovered in the event of a failure ofthe primary data store whereas a second available storage service mayensure that the dataset that is associated with the user will beprotected such that any data that is more than 5 minutes old can berecovered in the event of a failure of the primary data store.

An additional example of storage services that may be presented to auser, selected by a user, and ultimately applied to a dataset associatedwith the user can include one or more data compliance services. Suchdata compliance services may be embodied, for example, as services thatmay be provided to consumers (i.e., a user) the data compliance servicesto ensure that the user's datasets are managed in a way to adhere tovarious regulatory requirements. For example, one or more datacompliance services may be offered to a user to ensure that the user'sdatasets are managed in a way so as to adhere to the General DataProtection Regulation (‘GDPR’), one or data compliance services may beoffered to a user to ensure that the user's datasets are managed in away so as to adhere to the Sarbanes-Oxley Act of 2002 (‘SOX’), or one ormore data compliance services may be offered to a user to ensure thatthe user's datasets are managed in a way so as to adhere to some otherregulatory act. In addition, the one or more data compliance servicesmay be offered to a user to ensure that the user's datasets are managedin a way so as to adhere to some non-governmental guidance (e.g., toadhere to best practices for auditing purposes), the one or more datacompliance services may be offered to a user to ensure that the user'sdatasets are managed in a way so as to adhere to a particular clients ororganizations requirements, and so on.

Consider an example in which a particular data compliance service isdesigned to ensure that a user's datasets are managed in a way so as toadhere to the requirements set forth in the GDPR. While a listing of allrequirements of the GDPR can be found in the regulation itself, for thepurposes of illustration, an example requirement set forth in the GDPRrequires that pseudonymization processes must be applied to stored datain order to transform personal data in such a way that the resultingdata cannot be attributed to a specific data subject without the use ofadditional information. For example, data encryption techniques can beapplied to render the original data unintelligible, and such dataencryption techniques cannot be reversed without access to the correctdecryption key. As such, the GDPR may require that the decryption key bekept separately from the pseudonymised data. One particular datacompliance service may be offered to ensure adherence to therequirements set forth in this paragraph.

In order to provide this particular data compliance service, the datacompliance service may be presented to a user (e.g., via a GUI) andselected by the user. In response to receiving the selection of theparticular data compliance service, one or more storage servicespolicies may be applied to a dataset associated with the user to carryout the particular data compliance service. For example, a storageservices policy may be applied requiring that the dataset be encryptedprior to be stored in a storage system, prior to being stored in a cloudenvironment, or prior to being stored elsewhere. In order to enforcethis policy, a requirement may be enforced not only requiring that thedataset be encrypted when stored, but a requirement may be put in placerequiring that the dataset be encrypted prior to transmitting thedataset (e.g., sending the dataset to another party). In such anexample, a storage services policy may also be put in place requiringthat any encryption keys used to encrypt the dataset are not stored onthe same system that stores the dataset itself. Readers will appreciatethat many other forms of data compliance services may be offered andimplemented in accordance with embodiments of the present disclosure.

The storage systems 374 a, 374 b, 374 c, 374 d, 374 n in the fleet ofstorage systems 376 may be managed collectively, for example, by one ormore fleet management modules. The fleet management modules may be partof or separate from the system management services module 384 depictedin FIG. 3E. The fleet management modules may perform tasks such asmonitoring the health of each storage system in the fleet, initiatingupdates or upgrades on one or more storage systems in the fleet,migrating workloads for loading balancing or other performance purposes,and many other tasks. As such, and for many other reasons, the storagesystems 374 a, 374 b, 374 c, 374 d, 374 n may be coupled to each othervia one or more data communications links in order to exchange databetween the storage systems 374 a, 374 b, 374 c, 374 d, 374 n.

The storage systems described herein may support various forms of datareplication. For example, two or more of the storage systems maysynchronously replicate a dataset between each other. In synchronousreplication, distinct copies of a particular dataset may be maintainedby multiple storage systems, but all accesses (e.g., a read) of thedataset should yield consistent results regardless of which storagesystem the access was directed to. For example, a read directed to anyof the storage systems that are synchronously replicating the datasetshould return identical results. As such, while updates to the versionof the dataset need not occur at exactly the same time, precautions mustbe taken to ensure consistent accesses to the dataset. For example, ifan update (e.g., a write) that is directed to the dataset is received bya first storage system, the update may only be acknowledged as beingcompleted if all storage systems that are synchronously replicating thedataset have applied the update to their copies of the dataset. In suchan example, synchronous replication may be carried out through the useof I/O forwarding (e.g., a write received at a first storage system isforwarded to a second storage system), communications between thestorage systems (e.g., each storage system indicating that it hascompleted the update), or in other ways.

In other embodiments, a dataset may be replicated through the use ofcheckpoints. In checkpoint-based replication (also referred to as‘nearly synchronous replication’), a set of updates to a dataset (e.g.,one or more write operations directed to the dataset) may occur betweendifferent checkpoints, such that a dataset has been updated to aspecific checkpoint only if all updates to the dataset prior to thespecific checkpoint have been completed. Consider an example in which afirst storage system stores a live copy of a dataset that is beingaccessed by users of the dataset. In this example, assume that thedataset is being replicated from the first storage system to a secondstorage system using checkpoint-based replication. For example, thefirst storage system may send a first checkpoint (at time t=0) to thesecond storage system, followed by a first set of updates to thedataset, followed by a second checkpoint (at time t=1), followed by asecond set of updates to the dataset, followed by a third checkpoint (attime t=2). In such an example, if the second storage system hasperformed all updates in the first set of updates but has not yetperformed all updates in the second set of updates, the copy of thedataset that is stored on the second storage system may be up-to-dateuntil the second checkpoint. Alternatively, if the second storage systemhas performed all updates in both the first set of updates and thesecond set of updates, the copy of the dataset that is stored on thesecond storage system may be up-to-date until the third checkpoint.Readers will appreciate that various types of checkpoints may be used(e.g., metadata only checkpoints), checkpoints may be spread out basedon a variety of factors (e.g., time, number of operations, an RPOsetting), and so on.

In other embodiments, a dataset may be replicated through snapshot-basedreplication (also referred to as ‘asynchronous replication’). Insnapshot-based replication, snapshots of a dataset may be sent from areplication source such as a first storage system to a replicationtarget such as a second storage system. In such an embodiment, eachsnapshot may include the entire dataset or a subset of the dataset suchas, for example, only the portions of the dataset that have changedsince the last snapshot was sent from the replication source to thereplication target. Readers will appreciate that snapshots may be senton-demand, based on a policy that takes a variety of factors intoconsideration (e.g., time, number of operations, an RPO setting), or insome other way.

The storage systems described above may, either alone or in combination,by configured to serve as a continuous data protection store. Acontinuous data protection store is a feature of a storage system thatrecords updates to a dataset in such a way that consistent images ofprior contents of the dataset can be accessed with a low timegranularity (often on the order of seconds, or even less), andstretching back for a reasonable period of time (often hours or days).These allow access to very recent consistent points in time for thedataset, and also allow access to access to points in time for a datasetthat might have just preceded some event that, for example, caused partsof the dataset to be corrupted or otherwise lost, while retaining closeto the maximum number of updates that preceded that event. Conceptually,they are like a sequence of snapshots of a dataset taken very frequentlyand kept for a long period of time, though continuous data protectionstores are often implemented quite differently from snapshots. A storagesystem implementing a data continuous data protection store may furtherprovide a means of accessing these points in time, accessing one or moreof these points in time as snapshots or as cloned copies, or revertingthe dataset back to one of those recorded points in time.

Over time, to reduce overhead, some points in the time held in acontinuous data protection store can be merged with other nearby pointsin time, essentially deleting some of these points in time from thestore. This can reduce the capacity needed to store updates. It may alsobe possible to convert a limited number of these points in time intolonger duration snapshots. For example, such a store might keep a lowgranularity sequence of points in time stretching back a few hours fromthe present, with some points in time merged or deleted to reduceoverhead for up to an additional day. Stretching back in the pastfurther than that, some of these points in time could be converted tosnapshots representing consistent point-in-time images from only everyfew hours.

Although some embodiments are described largely in the context of astorage system, readers of skill in the art will recognize thatembodiments of the present disclosure may also take the form of acomputer program product disposed upon computer readable storage mediafor use with any suitable processing system. Such computer readablestorage media may be any storage medium for machine-readableinformation, including magnetic media, optical media, solid-state media,or other suitable media. Examples of such media include magnetic disksin hard drives or diskettes, compact disks for optical drives, magnetictape, and others as will occur to those of skill in the art. Personsskilled in the art will immediately recognize that any computer systemhaving suitable programming means will be capable of executing the stepsdescribed herein as embodied in a computer program product. Personsskilled in the art will recognize also that, although some of theembodiments described in this specification are oriented to softwareinstalled and executing on computer hardware, nevertheless, alternativeembodiments implemented as firmware or as hardware are well within thescope of the present disclosure.

In some examples, a non-transitory computer-readable medium storingcomputer-readable instructions may be provided in accordance with theprinciples described herein. The instructions, when executed by aprocessor of a computing device, may direct the processor and/orcomputing device to perform one or more operations, including one ormore of the operations described herein. Such instructions may be storedand/or transmitted using any of a variety of known computer-readablemedia.

A non-transitory computer-readable medium as referred to herein mayinclude any non-transitory storage medium that participates in providingdata (e.g., instructions) that may be read and/or executed by acomputing device (e.g., by a processor of a computing device). Forexample, a non-transitory computer-readable medium may include, but isnot limited to, any combination of non-volatile storage media and/orvolatile storage media. Exemplary non-volatile storage media include,but are not limited to, read-only memory, flash memory, a solid-statedrive, a magnetic storage device (e.g., a hard disk, a floppy disk,magnetic tape, etc.), ferroelectric random-access memory (“RAM”), and anoptical disc (e.g., a compact disc, a digital video disc, a Blu-raydisc, etc.). Exemplary volatile storage media include, but are notlimited to, RAM (e.g., dynamic RAM).

For further explanation, FIG. 4 sets forth a block diagram illustratinga plurality of storage systems (402, 404, 406) that support a podaccording to some embodiments of the present disclosure. Althoughdepicted in less detail, the storage systems (402, 404, 406) depicted inFIG. 4 may be similar to the storage systems described above withreference to FIGS. 1A-1D, FIGS. 2A-2G, FIGS. 3A-3B, or any combinationthereof. In fact, the storage systems (402, 404, 406) depicted in FIG. 4may include the same, fewer, or additional components as the storagesystems described above.

In the example depicted in FIG. 4, each of the storage systems (402,404, 406) is depicted as having at least one computer processor (408,410, 412), computer memory (414, 416, 418), and computer storage (420,422, 424). Although in some embodiments the computer memory (414, 416,418) and the computer storage (420, 422, 424) may be part of the samehardware devices, in other embodiments the computer memory (414, 416,418) and the computer storage (420, 422, 424) may be part of differenthardware devices. The distinction between the computer memory (414, 416,418) and the computer storage (420, 422, 424) in this particular examplemay be that the computer memory (414, 416, 418) is physically proximateto the computer processors (408, 410, 412) and may store computerprogram instructions that are executed by the computer processors (408,410, 412), while the computer storage (420, 422, 424) is embodied asnon-volatile storage for storing user data, metadata describing the userdata, and so on. Referring to the example above in FIG. 1A, for example,the computer processors (408, 410, 412) and computer memory (414, 416,418) for a particular storage system (402, 404, 406) may reside withinone of more of the controllers (110A-110D) while the attached storagedevices (171A-171F) may serve as the computer storage (420, 422, 424)within a particular storage system (402, 404, 406).

In the example depicted in FIG. 4, the depicted storage systems (402,404, 406) may attach to one or more pods (430, 432) according to someembodiments of the present disclosure. Each of the pods (430, 432)depicted in FIG. 4 can include a dataset (426, 428). For example, afirst pod (430) that three storage systems (402, 404, 406) have attachedto includes a first dataset (426) while a second pod (432) that twostorage systems (404, 406) have attached to includes a second dataset(428). In such an example, when a particular storage system attaches toa pod, the pod's dataset is copied to the particular storage system andthen kept up to date as the dataset is modified. Storage systems can beremoved from a pod, resulting in the dataset being no longer kept up todate on the removed storage system. In the example depicted in FIG. 4,any storage system which is active for a pod (it is an up-to-date,operating, non-faulted member of a non-faulted pod) can receive andprocess requests to modify or read the pod's dataset.

In the example depicted in FIG. 4, each pod (430, 432) may also includea set of managed objects and management operations, as well as a set ofaccess operations to modify or read the dataset (426, 428) that isassociated with the particular pod (430, 432). In such an example, themanagement operations may modify or query managed objects equivalentlythrough any of the storage systems. Likewise, access operations to reador modify the dataset may operate equivalently through any of thestorage systems. In such an example, while each storage system stores aseparate copy of the dataset as a proper subset of the datasets storedand advertised for use by the storage system, the operations to modifymanaged objects or the dataset performed and completed through any onestorage system are reflected in subsequent management objects to querythe pod or subsequent access operations to read the dataset.

Readers will appreciate that pods may implement more capabilities thanjust a clustered synchronously replicated dataset. For example, pods canbe used to implement tenants, whereby datasets are in some way securelyisolated from each other. Pods can also be used to implement virtualarrays or virtual storage systems where each pod is presented as aunique storage entity on a network (e.g., a Storage Area Network, orInternet Protocol network) with separate addresses. In the case of amulti-storage-system pod implementing a virtual storage system, allphysical storage systems associated with the pod may present themselvesas in some way the same storage system (e.g., as if the multiplephysical storage systems were no different than multiple network portsinto a single storage system).

Readers will appreciate that pods may also be units of administration,representing a collection of volumes, file systems, object/analyticstores, snapshots, and other administrative entities, where makingadministrative changes (e.g., name changes, property changes, managingexports or permissions for some part of the pod's dataset), on any onestorage system is automatically reflected to all active storage systemsassociated with the pod. In addition, pods could also be units of datacollection and data analysis, where performance and capacity metrics arepresented in ways that aggregate across all active storage systems forthe pod, or that call out data collection and analysis separately foreach pod, or perhaps presenting each attached storage system'scontribution to the incoming content and performance for each a pod.

One model for pod membership may be defined as a list of storagesystems, and a subset of that list where storage systems are consideredto be in-sync for the pod. A storage system may be considered to bein-sync for a pod if it is at least within a recovery of havingidentical idle content for the last written copy of the datasetassociated with the pod. Idle content is the content after anyin-progress modifications have completed with no processing of newmodifications. Sometimes this is referred to as “crash recoverable”consistency. Recovery of a pod carries out the process of reconcilingdifferences in applying concurrent updates to in-sync storage systems inthe pod. Recovery can resolve any inconsistencies between storagesystems in the completion of concurrent modifications that had beenrequested to various members of the pod but that were not signaled toany requestor as having completed successfully. Storage systems that arelisted as pod members but that are not listed as in-sync for the pod canbe described as “detached” from the pod. Storage systems that are listedas pod members, are in-sync for the pod, and are currently available foractively serving data for the pod are “online” for the pod.

Each storage system member of a pod may have its own copy of themembership, including which storage systems it last knew were in-sync,and which storage systems it last knew comprised the entire set of podmembers. To be online for a pod, a storage system must consider itselfto be in-sync for the pod and must be communicating with all otherstorage systems it considers to be in-sync for the pod. If a storagesystem can't be certain that it is in-sync and communicating with allother storage systems that are in-sync, then it must stop processing newincoming requests for the pod (or must complete them with an error orexception) until it can be certain that it is in-sync and communicatingwith all other storage systems that are in-sync. A first storage systemmay conclude that a second paired storage system should be detached,which will allow the first storage system to continue since it is nowin-sync with all storage systems now in the list. But, the secondstorage system must be prevented from concluding, alternatively, thatthe first storage system should be detached and with the second storagesystem continuing operation. This would result in a “split brain”condition that can lead to irreconcilable datasets, dataset corruption,or application corruption, among other dangers.

The situation of needing to determine how to proceed when notcommunicating with paired storage systems can arise while a storagesystem is running normally and then notices lost communications, whileit is currently recovering from some previous fault, while it isrebooting or resuming from a temporary power loss or recoveredcommunication outage, while it is switching operations from one set ofstorage system controller to another set for whatever reason, or duringor after any combination of these or other kinds of events. In fact, anytime a storage system that is associated with a pod can't communicatewith all known non-detached members, the storage system can either waitbriefly until communications can be established, go offline and continuewaiting, or it can determine through some means that it is safe todetach the non-communicating storage system without risk of incurring asplit brain due to the non-communicating storage system concluding thealternative view, and then continue. If a safe detach can happen quicklyenough, the storage system can remain online for the pod with littlemore than a short delay and with no resulting application outages forapplications that can issue requests to the remaining online storagesystems.

One example of this situation is when a storage system may know that itis out-of-date. That can happen, for example, when a first storagesystem is first added to a pod that is already associated with one ormore storage systems, or when a first storage system reconnects toanother storage system and finds that the other storage system hadalready marked the first storage system as detached. In this case, thisfirst storage system will simply wait until it connects to some otherset of storage systems that are in-sync for the pod.

This model demands some degree of consideration for how storage systemsare added to or removed from pods or from the in-sync pod members list.Since each storage system will have its own copy of the list, and sincetwo independent storage systems can't update their local copy at exactlythe same time, and since the local copy is all that is available on areboot or in various fault scenarios, care must be taken to ensure thattransient inconsistencies don't cause problems. For example, if onestorage systems is in-sync for a pod and a second storage system isadded, then if the second storage system is updated to list both storagesystems as in-sync first, then if there is a fault and a restart of bothstorage systems, the second might startup and wait to connect to thefirst storage system while the first might be unaware that it should orcould wait for the second storage system. If the second storage systemthen responds to an inability to connect with the first storage systemby going through a process to detach it, then it might succeed incompleting a process that the first storage system is unaware of,resulting in a split brain. As such, it may be necessary to ensure thatstorage systems won't disagree inappropriately on whether they might optto go through a detach process if they aren't communicating.

One way to ensure that storage systems won't disagree inappropriately onwhether they might opt to go through a detach process if they aren'tcommunicating is to ensure that when adding a new storage system to thein-sync member list for a pod, the new storage system first stores thatit is a detached member (and perhaps that it is being added as anin-sync member). Then, the existing in-sync storage systems can locallystore that the new storage system is an in-sync pod member before thenew storage system locally stores that same fact. If there is a set ofreboots or network outages prior to the new storage system storing itsin-sync status, then the original storage systems may detach the newstorage system due to non-communication, but the new storage system willwait. A reverse version of this change might be needed for removing acommunicating storage system from a pod: first the storage system beingremoved stores that it is no longer in-sync, then the storage systemsthat will remain store that the storage system being removed is nolonger in-sync, then all storage systems delete the storage system beingremoved from their pod membership lists. Depending on theimplementation, an intermediate persisted detached state may not benecessary. Whether or not care is required in local copies of membershiplists may depend on the model storage systems use for monitoring eachother or for validating their membership. If a consensus model is usedfor both, or if an external system (or an external distributed orclustered system) is used to store and validate pod membership, theninconsistencies in locally stored membership lists may not matter.

When communications fail or one or several storage systems in a podfail, or when a storage system starts up (or fails over to a secondarycontroller) and can't communicate with paired storage systems for a pod,and it is time for one or more storage systems to decide to detach oneor more paired storage systems, some algorithm or mechanism must beemployed to decide that it is safe to do so and to follow through on thedetach. One means of resolving detaches is use a majority (or quorum)model for membership. With three storage systems, as long as two arecommunicating, they can agree to detach a third storage system thatisn't communicating, but that third storage system cannot by itselfchoose to detach either of the other two. Confusion can arise whenstorage system communication is inconsistent. For example, storagesystem A might be communicating with storage system B but not C, whilestorage system B might be communicating with both A and C. So, A and Bcould detach C, or B and C could detach A, but more communicationbetween pod members may be needed to figure this out.

Care needs to be taken in a quorum membership model when adding andremoving storage systems. For example, if a fourth storage system isadded, then a “majority” of storage systems is at that point three. Thetransition from three storage systems (with two required for majority)to a pod including a fourth storage system (with three required formajority) may require something similar to the model describedpreviously for carefully adding a storage system to the in-sync list.For example, the fourth storage system might start in an attaching statebut not yet attached where it would never instigate a vote over quorum.Once in that state, the original three pod members could each be updatedto be aware of the fourth member and the new requirement for a threestorage system majority to detach a fourth. Removing a storage systemfrom a pod might similarly move that storage system to a locally stored“detaching” state before updating other pod members. A variant schemefor this is to use a distributed consensus mechanism such as PAXOS orRAFT to implement any membership changes or to process detach requests.

Another means of managing membership transitions is to use an externalsystem that is outside of the storage systems themselves to handle podmembership. In order to become online for a pod, a storage system mustfirst contact the external pod membership system to verify that it isin-sync for the pod. Any storage system that is online for a pod shouldthen remain in communication with the pod membership system and shouldwait or go offline if it loses communication. An external pod membershipmanager could be implemented as a highly available cluster using variouscluster tools, such as Oracle RAC, Linux HA, VERITAS Cluster Server,IBM's HACMP, or others. An external pod membership manager could alsouse distributed configuration tools such as Etcd or Zookeeper, or areliable distributed database such as Amazon's DynamoDB.

In the example depicted in FIG. 4, the depicted storage systems (402,404, 406) may receive a request to read a portion of the dataset (426,428) and process the request to read the portion of the dataset locallyaccording to some embodiments of the present disclosure. Readers willappreciate that although requests to modify (e.g., a write operation)the dataset (426, 428) require coordination between the storage systems(402, 404, 406) in a pod, as the dataset (426, 428) should be consistentacross all storage systems (402, 404, 406) in a pod, responding to arequest to read a portion of the dataset (426, 428) does not requiresimilar coordination between the storage systems (402, 404, 406). Assuch, a particular storage system that receives a read request mayservice the read request locally by reading a portion of the dataset(426, 428) that is stored within the storage system's storage devices,with no synchronous communication with other storage systems in the pod.Read requests received by one storage system for a replicated dataset ina replicated cluster are expected to avoid any communication in the vastmajority of cases, at least when received by a storage system that isrunning within a cluster that is also running nominally. Such readsshould normally be processed simply by reading from the local copy of aclustered dataset with no further interaction required with otherstorage systems in the cluster.

Readers will appreciate that the storage systems may take steps toensure read consistency such that a read request will return the sameresult regardless of which storage system processes the read request.For example, the resulting clustered dataset content for any set ofupdates received by any set of storage systems in the cluster should beconsistent across the cluster, at least at any time updates are idle(all previous modifying operations have been indicated as complete andno new update requests have been received and processed in any way).More specifically, the instances of a clustered dataset across a set ofstorage systems can differ only as a result of updates that have not yetcompleted. This means, for example, that any two write requests whichoverlap in their volume block range, or any combination of a writerequest and an overlapping snapshot, compare-and-write, or virtual blockrange copy, must yield a consistent result on all copies of the dataset.Two operations should not yield a result as if they happened in oneorder on one storage system and a different order on another storagesystem in the replicated cluster.

Furthermore, read requests can be made time order consistent. Forexample, if one read request is received on a replicated cluster andcompleted and that read is then followed by another read request to anoverlapping address range which is received by the replicated clusterand where one or both reads in any way overlap in time and volumeaddress range with a modification request received by the replicatedcluster (whether any of the reads or the modification are received bythe same storage system or a different storage system in the replicatedcluster), then if the first read reflects the result of the update thenthe second read should also reflect the results of that update, ratherthan possibly returning data that preceded the update. If the first readdoes not reflect the update, then the second read can either reflect theupdate or not. This ensures that between two read requests “time” for adata segment cannot roll backward.

In the example depicted in FIG. 4, the depicted storage systems (402,404, 406) may also detect a disruption in data communications with oneor more of the other storage systems and determine whether to theparticular storage system should remain in the pod. A disruption in datacommunications with one or more of the other storage systems may occurfor a variety of reasons. For example, a disruption in datacommunications with one or more of the other storage systems may occurbecause one of the storage systems has failed, because a networkinterconnect has failed, or for some other reason. An important aspectof synchronous replicated clustering is ensuring that any fault handlingdoesn't result in unrecoverable inconsistencies, or any inconsistency inresponses. For example, if a network fails between two storage systems,at most one of the storage systems can continue processing newlyincoming I/O requests for a pod. And, if one storage system continuesprocessing, the other storage system can't process any new requests tocompletion, including read requests.

In the example depicted in FIG. 4, the depicted storage systems (402,404, 406) may also determine whether the particular storage systemshould remain in the pod in response to detecting a disruption in datacommunications with one or more of the other storage systems. Asmentioned above, to be ‘online’ as part of a pod, a storage system mustconsider itself to be in-sync for the pod and must be communicating withall other storage systems it considers to be in-sync for the pod. If astorage system can't be certain that it is in-sync and communicatingwith all other storage systems that are in-sync, then it may stopprocessing new incoming requests to access the dataset (426, 428). Assuch, the storage system may determine whether to the particular storagesystem should remain online as part of the pod, for example, bydetermining whether it can communicate with all other storage systems itconsiders to be in-sync for the pod (e.g., via one or more testmessages), by determining whether the all other storage systems itconsiders to be in-sync for the pod also consider the storage system tobe attached to the pod, through a combination of both steps where theparticular storage system must confirm that it can communicate with allother storage systems it considers to be in-sync for the pod and thatall other storage systems it considers to be in-sync for the pod alsoconsider the storage system to be attached to the pod, or through someother mechanism.

In the example depicted in FIG. 4, the depicted storage systems (402,404, 406) may also keep the dataset on the particular storage systemaccessible for management and dataset operations in response todetermining that the particular storage system should remain in the pod.The storage system may keep the dataset (426, 428) on the particularstorage system accessible for management and dataset operations, forexample, by accepting requests to access the version of the dataset(426, 428) that is stored on the storage system and processing suchrequests, by accepting and processing management operations associatedwith the dataset (426, 428) that are issued by a host or authorizedadministrator, by accepting and processing management operationsassociated with the dataset (426, 428) that are issued by one of theother storage systems, or in some other way.

In the example depicted in FIG. 4, the depicted storage systems (402,404, 406) may, however, make the dataset on the particular storagesystem inaccessible for management and dataset operations in response todetermining that the particular storage system should not remain in thepod. The storage system may make the dataset (426, 428) on theparticular storage system inaccessible for management and datasetoperations, for example, by rejecting requests to access the version ofthe dataset (426, 428) that is stored on the storage system, byrejecting management operations associated with the dataset (426, 428)that are issued by a host or other authorized administrator, byrejecting management operations associated with the dataset (426, 428)that are issued by one of the other storage systems in the pod, or insome other way.

In the example depicted in FIG. 4, the depicted storage systems (402,404, 406) may also detect that the disruption in data communicationswith one or more of the other storage systems has been repaired and makethe dataset on the particular storage system accessible for managementand dataset operations. The storage system may detect that thedisruption in data communications with one or more of the other storagesystems has been repaired, for example, by receiving a message from theone or more of the other storage systems. In response to detecting thatthe disruption in data communications with one or more of the otherstorage systems has been repaired, the storage system may make thedataset (426, 428) on the particular storage system accessible formanagement and dataset operations once the previously detached storagesystem has been resynchronized with the storage systems that remainedattached to the pod.

In the example depicted in FIG. 4, the depicted storage systems (402,404, 406) may also go offline from the pod such that the particularstorage system no longer allows management and dataset operations. Thedepicted storage systems (402, 404, 406) may go offline from the podsuch that the particular storage system no longer allows management anddataset operations for a variety of reasons. For example, the depictedstorage systems (402, 404, 406) may also go offline from the pod due tosome fault with the storage system itself, because an update or someother maintenance is occurring on the storage system, due tocommunications faults, or for many other reasons. In such an example,the depicted storage systems (402, 404, 406) may subsequently update thedataset on the particular storage system to include all updates to thedataset since the particular storage system went offline and go backonline with the pod such that the particular storage system allowsmanagement and dataset operations, as will be described in greaterdetail in the resynchronization sections included below.

In the example depicted in FIG. 4, the depicted storage systems (402,404, 406) may also identifying a target storage system forasynchronously receiving the dataset, where the target storage system isnot one of the plurality of storage systems across which the dataset issynchronously replicated. Such a target storage system may represent,for example, a backup storage system, as some storage system that makesuse of the synchronously replicated dataset, and so on. In fact,synchronous replication can be leveraged to distribute copies of adataset closer to some rack of servers, for better local readperformance. One such case is smaller top-of-rack storage systemssymmetrically replicated to larger storage systems that are centrallylocated in the data center or campus and where those larger storagesystems are more carefully managed for reliability or are connected toexternal networks for asynchronous replication or backup services.

In the example depicted in FIG. 4, the depicted storage systems (402,404, 406) may also identify a portion of the dataset that is not beingasynchronously replicated to the target storage system by any of theother storages systems and asynchronously replicate, to the targetstorage system, the portion of the dataset that is not beingasynchronously replicated to the target storage system by any of theother storages systems, wherein the two or more storage systemscollectively replicate the entire dataset to the target storage system.In such a way, the work associated with asynchronously replicating aparticular dataset may be split amongst the members of a pod, such thateach storage system in a pod is only responsible for asynchronouslyreplicating a subset of a dataset to the target storage system.

In the example depicted in FIG. 4, the depicted storage systems (402,404, 406) may also detach from the pod, such that the particular storagesystem that detaches from the pod is no longer included in the set ofstorage systems across which the dataset is synchronously replicated.For example, if storage system (404) in FIG. 4 detached from the pod(430) illustrated in FIG. 4, the pod (430) would only include storagesystems (402, 406) as the storage systems across which the dataset (426)that is included in the pod (430) would be synchronously replicatedacross. In such an example, detaching the storage system from the podcould also include removing the dataset from the particular storagesystem that detached from the pod. Continuing with the example where thestorage system (404) in FIG. 4 detached from the pod (430) illustratedin FIG. 4, the dataset (426) that is included in the pod (430) could bedeleted or otherwise removed from the storage system (404).

Readers will appreciate that there are a number of unique administrativecapabilities enabled by the pod model that can further be supported.Also, the pod model itself introduces some issues that can be addressedby an implementation. For example, when a storage system is offline fora pod, but is otherwise running, such as because an interconnect failedand another storage system for the pod won out in mediation, there maystill be a desire or need to access the offline pod's dataset on theoffline storage system. One solution may be simply to enable the pod insome detached mode and allow the dataset to be accessed. However, thatsolution can be dangerous and that solution can cause the pod's metadataand data to be much more difficult to reconcile when the storage systemsdo regain communication. Furthermore, there could still be a separatepath for hosts to access the offline storage system as well as the stillonline storage systems. In that case, a host might issue I/O to bothstorage systems even though they are no longer being kept in sync,because the host sees target ports reporting volumes with the sameidentifiers and the host I/O drivers presume it sees additional paths tothe same volume. This can result in fairly damaging data corruption asreads and writes issued to both storage systems are no longer consistenteven though the host presumes they are. As a variant of this case, in aclustered application, such as a shared storage clustered database, theclustered application running on one host might be reading or writing toone storage system and the same clustered application running on anotherhost might be reading or writing to the “detached” storage system, yetthe two instances of the clustered application are communicating betweeneach other on the presumption that the dataset they each see is entirelyconsistent for completed writes. Since they aren't consistent, thatpresumption is violated and the application's dataset (e.g., thedatabase) can quickly end up being corrupted.

One way to solve both of these problems is to allow for an offline pod,or perhaps a snapshot of an offline pod, to be copied to a new pod withnew volumes that have sufficiently new identities that host I/O driversand clustered applications won't confuse the copied volumes as being thesame as the still online volumes on another storage system. Since eachpod maintains a complete copy of the dataset, which is crash consistentbut perhaps slightly different from the copy of the pod dataset onanother storage system, and since each pod has an independent copy ofall data and metadata needed to operate on the pod content, it is astraightforward problem to make a virtual copy of some or all volumes orsnapshots in the pod to new volumes in a new pod. In a logical extentgraph implementation, for example, all that is needed is to define newvolumes in a new pod which reference logical extent graphs from thecopied pod associated with the pod's volumes or snapshots, and with thelogical extent graphs being marked as copy on write. The new volumesshould be treated as new volumes, similarly to how volume snapshotscopied to a new volume might be implemented. Volumes may have the sameadministrative name, though within a new pod namespace. But, they shouldhave different underlying identifiers, and differing logical unitidentifiers from the original volumes.

In some cases it may be possible to use virtual network isolationtechniques (for example, by creating a virtual LAN in the case of IPnetworks or a virtual SAN in the case of fiber channel networks) in sucha way that isolation of volumes presented to some interfaces can beassured to be inaccessible from host network interfaces or host SCSIinitiator ports that might also see the original volumes. In such cases,it may be safe to provide the copies of volumes with the same SCSI orother storage identifiers as the original volumes. This could be used,for example, in cases where the applications expect to see a particularset of storage identifiers in order to function without an undue burdenin reconfiguration.

Some of the techniques described herein could also be used outside of anactive fault context to test readiness for handling faults. Readinesstesting (sometimes referred to as “fire drills”) is commonly requiredfor disaster recovery configurations, where frequent and repeatedtesting is considered a necessity to ensure that most or all aspects ofa disaster recovery plan are correct and account for any recent changesto applications, datasets, or changes in equipment. Readiness testingshould be non-disruptive to current production operations, includingreplication. In many cases the real operations can't actually be invokedon the active configuration, but a good way to get close is to usestorage operations to make copies of production datasets, and thenperhaps couple that with the use of virtual networking, to create anisolated environment containing all data that is believed necessary forthe important applications that must be brought up successfully in casesof disasters. Making such a copy of a synchronously replicated (or evenan asynchronously replicated) dataset available within a site (orcollection of sites) that is expected to perform a disaster recoveryreadiness test procedure and then starting the important applications onthat dataset to ensure that it can startup and function is a great tool,since it helps ensure that no important parts of the applicationdatasets were left out in the disaster recovery plan. If necessary, andpractical, this could be coupled with virtual isolated networks coupledperhaps with isolated collection of physical or virtual machines, to getas close as possible to a real world disaster recovery takeoverscenario. Virtually copying a pod (or set of pods) to another pod as apoint-in-time image of the pod datasets immediately creates an isolateddataset that contains all the copied elements and that can then beoperated on essentially identically to the originally pods, as well asallowing isolation to a single site (or a few sites) separately from theoriginal pod. Further, these are fast operations and they can be torndown and repeated easily allowing testing to repeated as often as isdesired.

Some enhancements could be made to get further toward perfect disasterrecovery testing. For example, in conjunction with isolated networks,SCSI logical unit identities or other types of identities could becopied into the target pod so that the test servers, virtual machines,and applications see the same identities. Further, the administrativeenvironment of the servers could be configured to respond to requestsfrom a particular virtual set of virtual networks to respond to requestsand operations on the original pod name so scripts don't require use oftest-variants with alternate “test” versions of object names. A furtherenhancement can be used in cases where the host-side serverinfrastructure that will take over in the case of a disaster takeovercan be used during a test. This includes cases where a disaster recoverydata center is completely stocked with alternative server infrastructurethat won't generally be used until directed to do so by a disaster. Italso includes cases where that infrastructure might be used fornon-critical operations (for example, running analytics on productiondata, or simply supporting application development or other functionswhich may be important but can be halted if needed for more criticalfunctions). Specifically, host definitions and configurations and theserver infrastructure that will use them can be set up as they will befor an actual disaster recovery takeover event and tested as part ofdisaster recovery takeover testing, with the tested volumes beingconnected to these host definitions from the virtual pod copy used toprovide a snapshot of the dataset. From the standpoint of the storagesystems involved, then, these host definitions and configurations usedfor testing, and the volume-to-host connection configurations usedduring testing, can be reused when an actual disaster takeover event istriggered, greatly minimizing the configuration differences between thetest configuration and the real configuration that will be used in caseof a disaster recovery takeover.

In some cases it may make sense to move volumes out of a first pod andinto a new second pod including just those volumes. The pod membershipand high availability and recovery characteristics can then be adjustedseparately, and administration of the two resulting pod datasets canthen be isolated from each other. An operation that can be done in onedirection should also be possible in the other direction. At some point,it may make sense to take two pods and merge them into one so that thevolumes in each of the original two pods will now track each other forstorage system membership and high availability and recoverycharacteristics and events. Both operations can be accomplished safelyand with reasonably minimal or no disruption to running applications byrelying on the characteristics suggested for changing mediation orquorum properties for a pod which were discussed in an earlier section.With mediation, for example, a mediator for a pod can be changed using asequence consisting of a step where each storage system in a pod ischanged to depend on both a first mediator and a second mediator andeach is then changed to depend only on the second mediator. If a faultoccurs in the middle of the sequence, some storage systems may depend onboth the first mediator and the second mediator, but in no case willrecovery and fault handling result in some storage systems dependingonly on the first mediator and other storage systems only depending onthe second mediator. Quorum can be handled similarly by temporarilydepending on winning against both a first quorum model and a secondquorum model in order to proceed to recovery. This may result in a veryshort time period where availability of the pod in the face of faultsdepend on additional resources, thus reducing potential availability,but this time period is very short and the reduction in availability isoften very little. With mediation, if the change in mediator parametersis nothing more than the change in the key used for mediation and themediation service used is the same, then the potential reduction inavailability is even less, since it now depends only on two calls to thesame service versus one call to that service, and rather than separatecalls to two separate services.

Readers will note that changing the quorum model may be quite complex.An additional step may be necessary where storage systems willparticipate in the second quorum model but won't depend on winning inthat second quorum model, which is then followed by the step of alsodepending on the second quorum model. This may be necessary to accountfor the fact that if only one system has processed the change to dependon the quorum model, then it will never win quorum since there willnever be a majority. With this model in place for changing the highavailability parameters (mediation relationship, quorum model, takeoverpreferences), we can create a safe procedure for these operations tosplit a pod into two or to join two pods into one. This may requireadding one other capability: linking a second pod to a first pod forhigh availability such that if two pods include compatible highavailability parameters the second pod linked to the first pod candepend on the first pod for determining and instigating detach-relatedprocessing and operations, offline and in-sync states, and recovery andresynchronization actions.

To split a pod into two, which is an operation to move some volumes intoa newly created pod, a distributed operation may be formed that can bedescribed as: form a second pod into which we will move a set of volumeswhich were previously in a first pod, copy the high availabilityparameters from the first pod into the second pod to ensure they arecompatible for linking, and link the second pod to the first pod forhigh availability. This operation may be encoded as messages and shouldbe implemented by each storage system in the pod in such a way that thestorage system ensures that the operation happens completely on thatstorage system or does not happen at all if processing is interrupted bya fault. Once all in-sync storage systems for the two pods haveprocessed this operation, the storage systems can then process asubsequent operation which changes the second pod so that it is nolonger linked to the first pod. As with other changes to highavailability characteristics for a pod, this involves first having eachin-sync storage system change to rely on both the previous model (thatmodel being that high availability is linked to the first pod) and thenew model (that model being its own now independent high availability).In the case of mediation or quorum, this means that storage systemswhich processed this change will first depend on mediation or quorumbeing achieved as appropriate for the first pod and will additionallydepend on a new separate mediation (for example, a new mediation key) orquorum being achieved for the second pod before the second pod canproceed following a fault that required mediation or testing for quorum.As with the previous description of changing quorum models, anintermediate step may set storage systems to participate in quorum forthe second pod before the step where storage systems participate in anddepend on quorum for the second pod. Once all in-sync storage systemshave processed the change to depend on the new parameters for mediationor quorum for both the first pod and the second pod, the split iscomplete.

Joining a second pod into a first pod operates essentially in reverse.First, the second pod must be adjusted to be compatible with the firstpod, by having an identical list of storage systems and by having acompatible high availability model. This may involve some set of stepssuch as those described elsewhere in this paper to add or remove storagesystems or to change mediator and quorum models. Depending onimplementation, it may be necessary only to reach an identical list ofstorage systems. Joining proceeds by processing an operation on eachin-sync storage system to link the second pod to the first pod for highavailability. Each storage system which processes that operation willthen depend on the first pod for high availability and then the secondpod for high availability. Once all in-sync storage systems for thesecond pod have processed that operation, the storage systems will theneach process a subsequent operation to eliminate the link between thesecond pod and the first pod, migrate the volumes from the second podinto the first pod, and delete the second pod. Host or applicationdataset access can be preserved throughout these operations, as long asthe implementation allows proper direction of host or applicationdataset modification or read operations to the volume by identity and aslong as the identity is preserved as appropriate to the storage protocolor storage model (for example, as long as logical unit identifiers forvolumes and use of target ports for accessing volumes are preserved inthe case of SCSI).

Migrating a volume between pods may present issues. If the pods have anidentical set of in-sync membership storage systems, then it may bestraightforward: temporarily suspend operations on the volumes beingmigrated, switch control over operations on those volumes to controllingsoftware and structures for the new pod, and then resume operations.This allows for a seamless migration with continuous uptime forapplications apart from the very brief operation suspension, providednetwork and ports migrate properly between pods. Depending on theimplementation, suspending operations may not even be necessary, or maybe so internal to the system that the suspension of operations has noimpact. Copying volumes between pods with different in-sync membershipsets is more of a problem. If the target pod for the copy has a subsetof in-sync members from the source pod, this isn't much of a problem: amember storage system can be dropped safely enough without having to domore work. But, if the target pod adds in-sync member storage systems tothe volume over the source pod, then the added storage systems must besynchronized to include the volume's content before they can be used.Until synchronized, this leaves the copied volumes distinctly differentfrom the already synchronized volumes, in that fault handling differsand request handling from the not yet synced member storage systemseither won't work or must be forwarded or won't be as fast because readswill have to traverse an interconnect. Also, the internal implementationwill have to handle some volumes being in sync and ready for faulthandling and others not being in sync.

There are other problems relating to reliability of the operation in theface of faults. Coordinating a migration of volumes betweenmulti-storage-system pods is a distributed operation. If pods are theunit of fault handling and recovery, and if mediation or quorum orwhatever means are used to avoid split-brain situations, then a switchin volumes from one pod with a particular set of state andconfigurations and relationships for fault handling, recovery, mediationand quorum to another then storage systems in a pod have to be carefulabout coordinating changes related to that handling for any volumes.Operations can't be atomically distributed between storage systems, butmust be staged in some way. Mediation and quorum models essentiallyprovide pods with the tools for implementing distributed transactionalatomicity, but this may not extend to inter-pod operations withoutadding to the implementation.

Consider even a simple migration of a volume from a first pod to asecond pod even for two pods that share the same first and secondstorage systems. At some point the storage systems will coordinate todefine that the volume is now in the second pod and is no longer in thefirst pod. If there is no inherent mechanism for transactional atomicityacross the storage systems for the two pods, then a naive implementationcould leave the volume in the first pod on the first storage system andthe second pod on the second storage system at the time of a networkfault that results in fault handling to detach storage systems from thetwo pods. If pods separately determine which storage system succeeds indetaching the other, then the result could be that the same storagesystem detaches the other storage system for both pods, in which casethe result of the volume migration recovery should be consistent, or itcould result in a different storage system detaching the other for thetwo pods. If the first storage system detaches the second storage systemfor the first pod and the second storage system detaches the firststorage system for the second pod, then recovery might result in thevolume being recovered to the first pod on the first storage system andinto the second pod on the second storage system, with the volume thenrunning and exported to hosts and storage applications on both storagesystems. If instead the second storage system detaches the first storagesystem for the first pod and first storage detaches the second storagesystem for the second pod, then recovery might result in the volumebeing discarded from the second pod by the first storage system and thevolume being discarded from the first pod by the second storage system,resulting in the volume disappearing entirely. If the pods a volume isbeing migrated between are on differing sets of storage systems, thenthings can get even more complicated.

A solution to these problems may be to use an intermediate pod alongwith the techniques described previously for splitting and joining pods.This intermediate pod may never be presented as visible managed objectsassociated with the storage systems. In this model, volumes to be movedfrom a first pod to a second pod are first split from the first pod intoa new intermediate pod using the split operation described previously.The storage system members for the intermediate pod can then be adjustedto match the membership of storage systems by adding or removing storagesystems from the pod, as necessary. Subsequently, the intermediate podcan be joined with the second pod.

For further explanation, FIG. 5 sets forth a flowchart illustrating anexample method of applying a rate limit across a plurality of storagesystems (504, 522) in accordance with some embodiments of the presentdisclosure. Although depicted in less detail, the storage systemsdepicted in FIG. 5 may be similar to the storage systems describedabove, as the storage systems (504, 522) may include any combination ofthe components contained in the storage systems described above. In theexample method depicted in FIG. 5, the storage systems are (504, 522)considered to be paired storage systems (502) as some degree ofcoordination may be required between the storage systems. For example, adataset may be synchronously replicated across the storage systems (504,522) as described above with reference to FIG. 4.

The example method depicted in FIG. 5 includes determining (506) a ratelimit for paired storage systems (504, 522). In the example depicted inFIG. 5, the rate limit specifies the cumulative amount of input/output(‘I/O’) operations that may be serviced by the paired storage systems(504, 522) during a predetermined period of time. The rate limit may beexpressed, for example, in terms of an amount of data per unit of time(e.g., GB/s). Consider an example in which the rate limit for the pairedstorage systems (504, 522) is 10 GB/s. In such an example, all datawritten to either of the paired storage systems (504, 522) and all dataread from either of the paired storage systems (504, 522) would counttowards the rate limit. For example, if the first storage system (504)serviced 1 GB of write operations directed to the storage system (504)during a particular one second period, the first storage system (504)serviced 2.5 GB of read operations directed to the storage system (504)during the particular one second period, the second storage system (522)serviced 1.5 GB of write operations directed to the storage system (522)during a particular one second period, and the second storage system(522) serviced 2 GB of read operations directed to the storage system(522) during the particular one second period, the paired storagesystems (504, 522) would have utilized 7 GB of its 10 GB limit duringthe particular one second period. Readers will appreciate that as thepaired storage systems (504, 522) hit their rate limits, the servicingof I/O operations may be throttled (e.g., via queueing the I/Ooperations) to ensure that the rate limit is not exceeded.

Readers will appreciate the rate limit rate limit for paired storagesystems (504, 522) may be distinct for one or more system-levelentities. For example, each stretched volume (described above) may havea distinct rate limit for that stretched volume (e.g., the rate limitfor a first stretched volume may be 1 GB/s and the rate limit for asecond stretched volume may be 1.5 GB/s). The rate limit for eachsystem-level entity may be determined, for example, by dividing thetotal amount of I/O operations that may be serviced by the pairedstorage systems (504, 522) by the number of system-level entities thatservice I/O operations in a weighted or unweighted manner.

In the example method depicted in FIG. 5, determining (506) a rate limitfor paired storage systems (504, 522) may be carried out, for example,by initially receiving the rate limit for paired storage systems (504,522) as a configuration parameter and storing such a value within eachof the storage systems (504, 522). In such an example, each storagesystem (504, 522) may periodically check its stored value to verify thatthe rate limit for the paired storage systems (504, 522) has not beenchanged.

The example method depicted in FIG. 5 also includes receiving (508), bya first storage system (504) from a second storage system (522), anamount (524) of I/O operations serviced by the second storage system(522) during a previous predetermined period of time. The amount (524)of I/O operations serviced by the second storage system (522) during aprevious predetermined period of time may be expressed, for example, interms of an amount of data that was read from or written to the secondstorage system (522) in order for the second storage system (522) toservice I/O operations received by the second storage system (522)during a previous predetermined period of time. In the example methoddepicted in FIG. 5, the previous predetermined period of time mayrepresent a polling interval that defines how frequently the secondstorage system (522) will send the first storage system (504)information describing the amount (524) of I/O operations serviced bythe second storage system (522). Consider an example in which the secondstorage system (522) is configured to send the first storage system(504) information describing the amount (524) of I/O operations servicedby the second storage system (522) every 250 milliseconds. In such anexample, every time that the second storage system (522) sends the firststorage system (504) information describing the amount (524) of I/Ooperations serviced by the second storage system (522) during a previouspredetermined period of time, the second storage system (522) will besending the first storage system (504) information describing the amount(524) of I/O operations serviced by the second storage system (522)during the most recent 250 milliseconds. Stated differently, theprevious predetermined period of time referenced above may be embodiedas the most recent polling interval.

Readers will appreciate that the ‘previous predetermined period of time’recited in step 508 and the ‘predetermined period of time’ recited instep 506 need not be the same duration. For example, if the rate limitfor the paired storage systems (504, 522) is 10 GB/s and the firststorage system (504) receives (508) information describing an amount(524) of I/O operations serviced by the second storage system (522)every 250 milliseconds, embodiments described herein may include makingconversions (e.g., viewing the rate limit as 2.5 GB per 250milliseconds) to create comparable values. In the example methoddepicted in FIG. 5, the first storage system (504) may receive (508) anamount (524) of I/O operations serviced by the second storage system(522) during a previous predetermined period of time from the secondstorage system (522) via one or more messages sent from the secondstorage system (522) to the first storage system (504) or in some otherway.

The example method depicted in FIG. 5 also includes determining (510)whether the amount (524) of I/O operations serviced by the secondstorage system (522) during a previous predetermined period of time isless than half of the rate limit for the paired storage systems (504,522). Continuing with the example described above where the rate limitfor the paired storage systems (504, 522) is 10 GB/s (or 2.5 GB per 250milliseconds), assume that the first storage system (504) received (508)information indicating that the second storage system (522) has serviced1.0 GB during the most recent 250 millisecond period of time. In such anexample, an affirmative (512) determination would be made that theamount (524) of I/O operations serviced by the second storage system(522) during a previous predetermined period of time is less than halfof the rate limit for the paired storage systems (504, 522), therebyindicating that the second storage system (522) is not utilizing itsfair share of the rate limit that would otherwise be reserved for use bythe second storage system (522).

The example method depicted in FIG. 5 also includes, responsive toaffirmatively (512) determining that the amount (524) of I/O operationsserviced by the second storage system (522) during the previouspredetermined period of time is less than half of the rate limit for thepaired storage systems (504, 522), setting (516) a local rate limit fora next predetermined period of time for the first storage system (504)to the difference between the rate limit for the paired storage systems(504, 522) and the amount (522) of I/O operations serviced by the secondstorage system (522) during the previous predetermined period of time.The local rate limit for the first storage system (504) specifies theamount of I/O operations that may be serviced by the first storagesystem (504) during a predetermined period of time. The rate limit maybe expressed, for example, in terms of an amount of data per unit oftime (e.g., GB/s).

Continuing with the example described where the rate limit for thepaired storage systems (504, 522) is 10 GB/s (or 2.5 GB per 250milliseconds) and the first storage system (504) received (508)information indicating that the second storage system (522) has serviced1.0 GB during the most recent 250 millisecond period of time, setting(516) a local rate limit for a next predetermined period of time for thefirst storage system (504) to the difference between the rate limit forthe paired storage systems (504, 522) and the amount (522) of I/Ooperations serviced by the second storage system (522) during theprevious predetermined period of time would result in the rate limit forthe first storage system being set to a value of 1.5 GB for the next 250millisecond interval. As such, because the second storage system (522)is not utilizing its fair share of the rate limit that would otherwisebe reserved for use by the second storage system (522), the firststorage system (504) may be given more than its fair share of the ratelimit that would otherwise be reserved for use by the first storagesystem (504).

The example method depicted in FIG. 5 also includes, responsive todetermining that the amount (524) of I/O operations serviced by thesecond storage system (522) during the previous predetermined period oftime is not (514) less than half of the rate limit for the pairedstorage systems (504, 522), setting (518) a local rate limit for a nextpredetermined period of time for the first storage system (504) to halfof the rate limit for the paired storage systems (504, 522). Continuingwith the example described above where the rate limit for the pairedstorage systems (504, 522) is 10 GB/s (or 2.5 GB per 250 milliseconds),assume that the first storage system (504) received (508) informationindicating that the second storage system (522) has serviced 1.5 GBduring the most recent 250 millisecond period of time. In such anexample, a determination would be made that the amount (524) of I/Ooperations serviced by the second storage system (522) during theprevious predetermined period of time is not (514) less than half of therate limit for the paired storage systems (504, 522), thereby indicatingthat the second storage system (522) is utilizing at least its fairshare of the rate limit that would otherwise be reserved for use by thesecond storage system (522).

Continuing with the example described where the rate limit for thepaired storage systems (504, 522) is 10 GB/s (or 2.5 GB per 250milliseconds) and the first storage system (504) received (508)information indicating that the second storage system (522) has serviced1.5 GB during the most recent 250 millisecond period of time, setting(518) a local rate limit for a next predetermined period of time for thefirst storage system (504) to half of the rate limit for the pairedstorage systems (504, 522) would result in the rate limit for the firststorage system being set to a value of 1.25 GB for the next 250millisecond interval. As such, because the second storage system (522)is utilizing at least its fair share of the rate limit that wouldotherwise be reserved for use by the second storage system (522), thefirst storage system (504) may not be given more than its fair share ofthe rate limit that would otherwise be reserved for use by the firststorage system (504).

Although depicted in less detail, each storage system depicted in FIG. 4may include a controller that services I/O operations directed to thestorage system (504, 522). In such an example, each controller mayinclude a plurality of nodes (e.g., a plurality of Non-uniform memoryaccess (NUMA) nodes) that handle each handle some subset of the I/Ooperations serviced by each controller. In such an example, each nodemay participate in an exchange of information that mirrors theinteractions of the storage systems described above. For example, afirst node in a first storage system (504) may determine a local ratelimit for the storage system (504) and may also receive, from a secondnode in the storage system (504), an amount of I/O operations servicedby the second node during a previous predetermined period of time. Thefirst node may subsequently determine whether the amount of I/Ooperations serviced by the second node during the previous predeterminedperiod of time is less than half of the local rate limit for the storagesystem (504). In response to determining that the amount of I/Ooperations serviced by the second node is less than half of the localrate limit for the storage system (504), the first node may set a localrate limit for a next predetermined period of time for the first node tothe difference between the local rate limit for the storage system (504)and the amount of I/O operations serviced by the second node during theprevious predetermined period of time. Alternatively; in response todetermining that the amount of I/O operations serviced by the secondnode during the previous predetermined period of time is not less thanhalf of the local rate limit for the storage system (504), the firstnode may set its local rate limit for a next predetermined period oftime to half of the local rate limit of the storage system (504). Thesecond node may similarly perform similar steps.

Readers will appreciate that through the use of the techniques describedabove, a hierarchy may essentially be created where a pair of storagesystems negotiate their individual local rate limits based on theactivity of the other storage system in the pair, and where pairs ofnodes within an individual storage system similarly negotiate theirlocal rate limits based on the activity of the other paired node andbased on the local rate limit of the storage system that the pair ofnodes reside within. As such, the rate limit for a first node within theparticular storage system may be set in dependence upon amount of I/Ooperations serviced by a second node within the particular storagesystem during a previous predetermined period of time and the local ratelimit for the particular storage system.

For further explanation, FIG. 6 sets forth a flowchart illustrating anadditional example method of applying a rate limit across a plurality ofstorage systems (504, 522) in accordance with some embodiments of thepresent disclosure. The example method depicted in FIG. 6 is similar tothe example method depicted in FIG. 5, as the example method depicted inFIG. 6 also includes determining (506) a rate limit for paired storagesystems (504, 522), receiving (508) an amount (524) of I/O operationsserviced by the second storage system (522) during a previouspredetermined period of time, determining (510) whether the amount (524)of I/O operations serviced by the second storage system (522) during aprevious predetermined period of time is less than half of the ratelimit for the paired storage systems (504, 522), and setting (516, 518)a local rate limit for a next predetermined period of time for the firststorage system (504).

The example method depicted in FIG. 6 also includes determining (602) arate limit for paired storage systems (504, 522), the rate limitspecifying the cumulative amount of I/O operations that may be servicedby the paired storage systems (504, 522) during a predetermined periodof time. Determining (602) a rate limit for paired storage systems (504,522) may be carried out, for example, by initially receiving the ratelimit for paired storage systems (504, 522) as a configuration parameterand storing such a value within each of the storage systems (504, 522).In such an example, each storage system (504, 522) may periodicallycheck its stored value to verify that the rate limit for the pairedstorage systems (504, 522) has not been changed.

The example method depicted in FIG. 6 also includes receiving (604), bythe second storage system (522) from the first storage system (504), anamount (616) of I/O operations serviced by the first storage system(504) during a previous predetermined period of time. The amount (616)of I/O operations serviced by the first storage system (504) during aprevious predetermined period of time may be expressed, for example, interms of an amount of data that was read from or written to the firststorage system (504) in order for the first storage system (504) toservice I/O operations received by the first storage system (504) duringa previous predetermined period of time. In the example method depictedin FIG. 6, the previous predetermined period of time may represent apolling interval that defines how frequently the first storage system(504) will send the second storage system (522) information describingthe amount (616) of I/O operations serviced by the first storage system(504). Consider an example in which the first storage system (504) isconfigured to send the second storage system (522) informationdescribing the amount (616) of I/O operations serviced by the firststorage system (504) every 250 milliseconds. In such an example, everytime that the first storage system (504) sends the second storage system(522) information describing the amount (616) of I/O operations servicedby the first storage system (504) during a previous predetermined periodof time, the first storage system (504) will be sending the secondstorage system (522) information describing the amount (616) of I/Ooperations serviced by the first storage system (504) during the mostrecent 250 milliseconds. Stated differently, the previous predeterminedperiod of time referenced above may be embodied as the most recentpolling interval.

The example method depicted in FIG. 6 also includes determining (606)whether the amount of I/O operations serviced by the first storagesystem (504) during a previous predetermined period of time is less thanhalf of the rate limit for the paired storage systems (504, 522).Continuing with the example described above where the rate limit for thepaired storage systems (504, 522) is 10 GB/s (or 2.5 GB per 250milliseconds), assume that the second storage system (522) received(604) information indicating that the first storage system (504) hasserviced 1.0 GB during the most recent 250 millisecond period of time.In such an example, an affirmative (612) determination would be madethat the amount (616) of I/O operations serviced by the first storagesystem (504) during a previous predetermined period of time is less thanhalf of the rate limit for the paired storage systems (504, 522),thereby indicating that the first storage system (504) is not utilizingits fair share of the rate limit that would otherwise be reserved foruse by the first storage system (504).

The example method depicted in FIG. 6 also includes, responsive toaffirmatively (612) determining that the amount (616) of I/O operationsserviced by the first storage system (504) during the previouspredetermined period of time is less than half of the rate limit for thepaired storage systems (504, 522), setting (608) a local a rate limitfor a next predetermined period of time for the second storage system(522) to the difference between the rate limit for the paired storagesystems (504, 522) and the amount (616) of I/O operations serviced bythe first storage system (504) during the previous predetermined periodof time. The local rate limit for the second storage system (522)specifies the amount of I/O operations that may be serviced by thesecond storage system (522) during a predetermined period of time. Therate limit may be expressed, for example, in terms of an amount of dataper unit of time (e.g., GB/s).

Continuing with the example described where the rate limit for thepaired storage systems (504, 522) is 10 GB/s (or 2.5 GB per 250milliseconds) and the second storage system (522) received (604)information indicating that the first storage system (504) has serviced1.0 GB during the most recent 250 millisecond period of time, setting(608) a local rate limit for a next predetermined period of time for thesecond storage system (522) to the difference between the rate limit forthe paired storage systems (504, 522) and the amount (616) of I/Ooperations serviced by the first storage system (504) during theprevious predetermined period of time would result in the rate limit forthe second storage system (522) being set to a value of 1.5 GB for thenext 250 millisecond interval. As such, because the first storage system(504) is not utilizing its fair share of the rate limit that wouldotherwise be reserved for use by the first storage system (504), thesecond storage system (522) may be given more than its fair share of therate limit that would otherwise be reserved for use by the secondstorage system (522).

The example method depicted in FIG. 6 also includes, responsive todetermining that the amount (616) of I/O operations serviced by thefirst storage system (504) during the previous predetermined period oftime is not (614) less than half of the rate limit for the pairedstorage systems (504, 522), setting (610) a local rate limit for a nextpredetermined period of time for the second storage system (522) to halfof the rate limit for the paired storage systems (504, 522). Continuingwith the example described above where the rate limit for the pairedstorage systems (504, 522) is 10 GB/s (or 2.5 GB per 250 milliseconds),assume that the second storage system (522) received (604) informationindicating that the first storage system (504) has serviced 1.5 GBduring the most recent 250 millisecond period of time. In such anexample, a determination would be made that the amount (616) of I/Ooperations serviced by the first storage system (504) during theprevious predetermined period of time is not (614) less than half of therate limit for the paired storage systems (504, 522), thereby indicatingthat the first storage system (504) is utilizing at least its fair shareof the rate limit that would otherwise be reserved for use by the firststorage system (504).

Continuing with the example described where the rate limit for thepaired storage systems (504, 522) is 10 GB/s (or 2.5 GB per 250milliseconds) and the second storage system (522) received (604)information indicating that the first storage system (504) has serviced1.5 GB during the most recent 250 millisecond period of time, setting(610) a local rate limit for a next predetermined period of time for thesecond storage system (522) to half of the rate limit for the pairedstorage systems (504, 522) would result in the rate limit for the secondstorage system (522) being set to a value of 1.25 GB for the next 250millisecond interval. As such, because the first storage system (504) isutilizing at least its fair share of the rate limit that would otherwisebe reserved for use by the first storage system (504), the secondstorage system (522) may not be given more than its fair share of therate limit that would otherwise be reserved for use by the secondstorage system (522).

For further explanation, FIG. 7 sets forth a flowchart illustrating anadditional example method of applying a rate limit across a plurality ofstorage systems (504, 522) in accordance with some embodiments of thepresent disclosure. The example method depicted in FIG. 7 is similar tothe example methods depicted in FIG. 5 and FIG. 6, as the example methoddepicted in FIG. 6 also includes determining (506) a rate limit forpaired storage systems (504, 522), receiving (508) an amount (524) ofI/O operations serviced by the second storage system (522) during aprevious predetermined period of time, determining (510) whether theamount (524) of I/O operations serviced by the second storage system(522) during a previous predetermined period of time is less than halfof the rate limit for the paired storage systems (504, 522), setting(516, 518) a local rate limit for a next predetermined period of timefor the first storage system (504), receiving (604) an amount (616) ofI/O operations serviced by the first storage system (504) during aprevious predetermined period of time, determining (606) whether theamount of I/O operations serviced by the first storage system (504)during a previous predetermined period of time is less than half of therate limit for the paired storage systems (504, 522), and setting (608,610) a local a rate limit for a next predetermined period of time forthe second storage system (522).

The example method depicted in FIG. 7 also includes sending (702), fromthe first storage system (504) to the second storage system (522) at apredetermined interval, information describing the amount (524) of I/Ooperations serviced by the first storage system (504) during a previouspredetermined period of time. The predetermined interval may be aconfigurable parameter and may represent the frequency at which each ofthe paired storage systems (504, 522) should send each other informationdescribing the amount of I/O operations that a particular storage systemserviced during a previous predetermined period of time.

Consider the example described above where the predetermined interval is250 milliseconds. In such an example, every 250 milliseconds the firststorage system (504) may send (702), to the second storage system (522),information describing the amount (524) of I/O operations serviced bythe first storage system (504) during the previous 250 milliseconds. Thefirst storage system (504) may send (702) such information to the secondstorage system (522), for example, via one or more messages, by writing(e.g., via RDMA) such information into a memory location monitored bythe second storage system (522), or in other ways. In order tofacilitate the regular exchange of such information between the pairedstorage systems (504, 522), the example method depicted in FIG. 7similarly includes sending (704), from the second storage system (524)to the first storage system (504) at the predetermined interval,information describing an amount (616) of I/O operations serviced by thesecond storage system (524) during the previous predetermined period oftime.

Readers will appreciate that some embodiments of the present disclosurecan include modifying the predetermined interval. In such an example, adetermination may be made that the predetermined interval is too long ortoo short, and as such, the predetermined interval may need to bemodified. As such, the predetermined interval represents a configurableparameter that may be tweaked, as necessary.

Readers will further appreciate that in some embodiments the sum of thelocal rate limit for the first storage system (504) and the local ratelimit for the second storage system (522) can exceed the rate limitspecifying the cumulative amount of I/O operations that may be servicedby the paired storage systems (504, 522) during the predetermined periodof time. Consider the example described above where the rate limit forthe paired storage systems (504, 522) is 10 GB/s. In such an example,assume that the predetermined interval upon which the storage systems(504, 522) exchange information describing the amount of I/O operationsserviced by each storage system (504, 522) during a previouspredetermined interval is set to a value of one second. Further assumethat neither storage system (504, 522) has processed any I/O operationsduring the most recent 1 second interval. In such an example, asdescribed in greater detail, each storage system will set its local ratelimit to 10 GB for the next 1 second interval, meaning that the storagesystems (504, 522) could collectively service 20 GB of I/O operations inthe next second, which is well above the 10 GB/s rate limit for thepaired storage systems (504, 522). Readers will appreciate that in suchan example, however, the sum of the local rate limit for the firststorage system (504) and the local rate limit for the second storagesystem (522) will quickly be adjusted to comply with the rate limitspecifying the cumulative amount of I/O operations that may be servicedby the paired storage systems (504, 522) after a subsequent exchange ofinformation between the paired storage systems (504, 522).

For further explanation, FIG. 8 sets forth a flowchart illustrating anadditional example method of applying a rate limit across a plurality ofstorage systems (504, 522) in accordance with some embodiments of thepresent disclosure. The example method depicted in FIG. 6 is similar tothe example method depicted in FIGS. 5-7, as the example method depictedin FIG. 8 also includes determining (506) a rate limit for pairedstorage systems (504, 522), receiving (508) an amount (524) of I/Ooperations serviced by the second storage system (522) during a previouspredetermined period of time, determining (510) whether the amount (524)of I/O operations serviced by the second storage system (522) during aprevious predetermined period of time is less than half of the ratelimit for the paired storage systems (504, 522), and setting (516, 518)a local rate limit for a next predetermined period of time for the firststorage system (504). Readers will further appreciate that the stepsintroduced in FIG. 8 could also be included in embodiments where theexchange of information is bi-directional as explicitly depicted in FIG.6 and FIG. 7.

Readers will appreciate that the steps described above may be performediteratively in accordance with a predetermined interval, such that thepaired storage systems (504, 522) are repeatedly exchanging informationdescribing the amount of I/O operations that they have processed and thepaired storage systems (504, 522) are repeatedly adjusting their localrate limits. At some point in time, however, one of the paired storagesystems (504, 522) may crash or otherwise become unreachable. As such,the example method depicted in FIG. 8 also includes detecting (802) thatthe first storage system (504) has not received information describingthe amount (524) of I/O operations serviced by the second storage system(522) during the previous predetermined period of time. Detecting (802)that the first storage system (504) has not received informationdescribing the amount (524) of I/O operations serviced by the secondstorage system (522) during the previous predetermined period of timemay be carried out, for example, by determining that no messages havebeen received from the second storage system (522) within an expectedperiod of time (e.g., the predetermined interval). In response todetecting (802) that the first storage system (504) has not receivedinformation describing the amount (524) of I/O operations serviced bythe second storage system (522) during the previous predetermined periodof time, the first storage system (504) may assume that the secondstorage system (522) is offline or otherwise unreachable. As such, theexample method depicted in FIG. 8 also includes setting (804) the localrate limit for the next predetermined period of time for the firststorage system (504) to the rate limit for the paired storage systems(504, 522).

Readers will appreciate that although the embodiments described aboverelate to embodiments where two storage systems are paired, in otherembodiments additional storage systems may be paired. For example, someembodiments can include a system of paired storage systems, where eachstorage system is configured to determine a rate limit for the system ofpaired storage systems and receive, from each of the other storagesystems in the system of paired storage systems, an amount of I/Ooperations serviced by the other storage system during a previouspredetermined period of time in a manner as described above. Readerswill appreciate that in some embodiments, rather than individuallyreceiving an amount of I/O operations serviced by each other storagesystem during a previous predetermined period of time, each storagesystem may simply receive the cumulative amount of I/O operationsserviced by each of the other storage systems in the system during aprevious predetermined period of time. Each storage system, in theembodiments described herein, may also be configured to determinewhether the cumulative amount of I/O operations serviced by each of theother storage systems in the system of paired storage systems during theprevious predetermined period of time is less than a fair share of I/Obandwidth for the other storage systems in the system of paired storagesystems.

In such embodiments, the ‘fair share’ of I/O bandwidth for eachindividual storage system would be equal to the rate limit for thesystem of paired storage systems divided by the number of storagesystems in the system of paired storage systems. For example, if thesystem of paired storage systems included 10 storage systems and therate limit for the system of paired storage systems was 10 GB/s, the‘fair share’ of I/O bandwidth for each individual storage system wouldbe 1 GB/s. As such, the fair share of I/O bandwidth for the otherstorage systems in the system of paired storage systems would be equalto the sum of the fair share of I/O bandwidth for each other storagesystem in the system of paired storage systems (9 GB/s in the exampledescribed in the preceding sentences.

In such embodiments, each storage system may be configured to set itslocal rate limit for a next predetermined period of time to thedifference between the rate limit for the system of paired storagesystems and the cumulative amount of I/O operations serviced by each ofthe other storage systems in the system of paired storage systems duringthe previous predetermined period of time. Each storage system may setits local rate limit in such a way in response to affirmativelydetermining that the cumulative amount of I/O operations serviced byeach of the other storage systems in the system of paired storagesystems during the previous predetermined period of time is less thanthe fair share of I/O bandwidth for the other storage systems in thesystem of paired storage systems. If a storage system determines thatthe cumulative amount of I/O operations serviced by each of the otherstorage systems in the system of paired storage systems during theprevious predetermined period of time is not less than the fair share ofI/O bandwidth for the other storage systems in the system of pairedstorage systems, however, each storage system may be configured to setits local rate limit for a next predetermined period of time to a fairshare of I/O bandwidth for each storage system.

Readers will appreciate that while the examples described in thepreceding paragraphs relates to embodiments where a ‘fair share’ of I/Obandwidth for each storage system is utilized, alternative embodimentsmay not include the notion of a ‘fair share’ but instead may rely onallowing for different storage systems to have access to unequalportions of the total rate limit for the system of storage systems. Insuch examples, each storage system may be configured to determinewhether the cumulative amount of I/O operations serviced by each of theother storage systems in the system during the previous predeterminedperiod of time is less than a ‘configurable share’ of I/O bandwidth forthe other storage systems in the system of paired storage systems. Insuch example, each of the storage systems may be associated with apredetermined share of I/O bandwidth that is essentially reserved forthe storage system, where the predetermined share of I/O bandwidth isdifferent for at least two of the storage systems. In such an example,the ‘configurable share’ of I/O bandwidth for the other storage systemsin the system of paired storage systems may be equal to the sum of thepredetermined share of I/O bandwidth for each other storage system. Insuch an example, each storage system may be configured to set its localrate limit for a next predetermined period of time to the differencebetween the rate limit for the system of paired storage systems and thecumulative amount of I/O operations serviced by each of the otherstorage systems in the system during the previous predetermined periodof time in response to affirmatively determining that the cumulativeamount of I/O operations serviced by each of the other storage systemsin the system during the previous predetermined period of time is lessthan a configurable share of I/O bandwidth for the other storage systemsin the system of paired storage systems. Likewise, each storage systemmay be configured to set its local rate limit for a next predeterminedperiod of time to its predetermined share of I/O bandwidth in responseto determining that the cumulative amount of I/O operations serviced byeach of the other storage systems in the system during the previouspredetermined period of time of time is not less than the configurableshare of I/O bandwidth for the other storage systems in the system ofpaired storage systems. Readers will appreciate that in otherembodiments, some hybrid between the ‘fair share’ and ‘configurableshare’ embodiments may be implemented.

In the embodiments described above, each storage system may beconfigured to perform many of the same steps as described above such as,for example, sending information describing the amount of I/O operationsserviced by the storage system during a previous predetermined period oftime to at least one of the other storage systems at a predeterminedinterval. Likewise, each storage system may be configured to modify thepredetermined interval as described above. Each storage system maysimilarly be configured to detecting that the storage system has notreceived information describing the amount of I/O operations serviced byat least one other storage system during the previous predeterminedperiod of time and may assume (for the purposes of making thedeterminations described above) that the at least one other storagesystem did not service any I/O operations during the previouspredetermined period of time.

For further explanation, FIG. 9 sets forth an illustrative example of adata frame header 900 according to some embodiments of the presentdisclosure. As will be described hereafter, fields within the data frameheader may be used to prioritize network traffic including sharing ofI/O bandwidth as described previously. Other types of data frame headersare possible and the illustrated data frame header 900 merely representsa header of a conventional Fibre Channel frame as known in the art.Fibre Channel is a high-speed data transfer protocol that providesin-order, lossless delivery of raw block data. Many types of servicesmay use Fibre Channel to provide a high-speed data connection. Differentservices may have different requirements of the Fibre Channel. Evenwithin a particular service, some entities may require a different levelof service than other entities. The header fields may be used toidentify a type of service and a priority level. For example, data frameheader 900 includes a type field 902 and a priority field 904. The typefield 902 can identify a type of service related to the data frame. Forexample, the type field 902 can be used to encode an application type.In particular, the type field 902 may identify the data frame as beingrelated to a virtual machine (VM). In the following description,reference will be made to VMs, but one of ordinary skill in the art willrecognize that an application container or other type of virtualizationmay be used in place the described virtual machine. The type field 902may merely identify the data frame as belonging to a VM or may identifythe data frame as belonging to a group of VM. The type field 902 mayidentify traffic as belonging to a VM through the use of a particularvalue recognized by a hardware vendor as belonging to a VM. In someinstances, the type field 902 may identify a specific VM through the useof a UUID, a reference to a table of VMs, or other lookup mechanism. Ininstances where the type field identifies a group of VMs, the type field902 may provide a reference to a table of VMs for looking up members ofa group, or may include some other type of lookup mechanism. Thepriority field 904 can be used to encode a relative priority for thedata frame. For example, a higher value in a priority field 904 maysignify traffic having a higher priority that traffic having a lowervalue in the priority field. These fields may then be used to manage thequality of service provided by a storage array and any interveningnetwork adapters to a VM. The identification of the VM in the data frameenables hardware vendors and storage area networks to manage quality ofservice and provide telemetry services for the VM.

For further explanation, FIG. 10 sets forth an illustrative example of astorage system 1000 comprising a host computing system 1002 thataccesses a volume stored at a first storage array 1008. The hostcomputing system 1002 can include virtual machines 1004 a-d managed by ahypervisor 1006. In some examples, the hypervisor 1006 may encode thefields identifying VM traffic and priority as described in relation toFIG. 9. A first storage fabric 1010 communicatively couples the hostcomputing system 1002 to the storage array 1008.

Communication between the host 1002 and the first storage array 1008 maybe implemented using Fibre Channel data frames having headers encodedwith VM identify and priority as described above. The first storagefabric 1010 comprises network hardware configured to prioritize orotherwise process the data frames based on the VM identity and priorityincluded in the header.

A second storage fabric 1014 may communicatively couple the firststorage array 1008 to a second storage array 1012. The second storagefabric 1014, like the first storage fabric 1010 comprises hardwareconfigured to prioritize or otherwise process data frames based on theVM identity and priority included in the header. In a conventionalsystem, communication between the first storage array 1008 and thesecond storage array 1012 is processed differently that traffic betweena virtual machine 1004 a-d and the first storage array 1008 since thedata frames are not sent or received by a virtual machine. For example,the VM identity field of the header may comprise a value indicatinggeneral traffic between storage arrays.

The system of FIG. 10, however, treats data frames related to a VMdifferently than standard traffic between storage arrays. For example, avolume related to a VM may be synchronously replicated between the firststorage array 1008 and the second storage array 1012. In such instances,the virtual machines 1004 a-d may be sensitive to how quickly writes canbe confirmed to both storage arrays 1008, 1012. Or in other examples, itmay be desirable to maintain a storage policy associated with the VM tobe maintained with other storage objects, such as replicas. To enablethe second storage fabric 1014 to maintain the quality of serviceassociated with the first storage fabric 1010, the first storage array1008 may encode data frames transmitted between the first storage array1008 and the second storage array 1012 with the same header fieldsrelated to priority as contained in data frame headers sent between avirtual machine 1004 a-d and the volume. For example, if the virtualmachine 1004 a-d sends a data frame having a header comprising anidentity field identifying VM traffic and a priority field indicating arelatively high priority, the first storage array 10087 may encode theheader of data frames related to the volume with the same identity fieldand priority field. Thus, the second storage fabric 1014 can maintainthe same quality of service provided by the first storage fabric 1010.In some examples, the same storage fabric may be used to communicatebetween the host and both storage arrays 1008, 1012. In such examples,the first storage array 1008 may still encode the headers with theidentity field and priority field for transmission over the singlestorage fabric.

In some examples, the first storage array 1008 may save the VM identityand the priority associated with the VM. The first storage array 1008may determine the virtual machine identity and the priority by readingthe identity field and the priority field of the data frame headers ofdata frame directed to the first storage array 1008. The first storagearray 1008 may then later use the stored VM identity and priority toencode the stored VM identity and priority to other data frame headerswhen transmitting data frames associated with the volume. Thus, thefirst storage array 1008 may replicate, copy, or otherwise transmit dataframes associated with the VM to other storage arrays or other devicesusing the same VM identifier and priority as the original trafficbetween a virtual machine and the first storage array 1008. By includingthe same VM identifier and priority, the same quality of service andtelemetry data may be extended to other storage fabrics.

For further explanation, FIG. 11 sets forth an illustrative example of astorage system 1100 comprising a first storage array 1102 and a secondstorage array 1104 communicatively coupled by a storage fabric 1106. Thefirst storage array 1102 may host volumes 1108 associated with VMs. Thevolumes 1108 may be arranged in groups of volumes 1108 such as a goldgroup 1110, a silver group 1112, and a bronze group 1114. The groups ofvolumes 1108 may be determined by a user as part of a storage policy andmay include other storage objects, be associated with a particulartenant accessing a group of volumes 1108, or may be associated with agroup of tenants accessing a group of volumes 1108.

The storage policy may identify a service level guaranteed to a tenantaccessing a group of volumes. The service policy may further identify aDisaster Recovery (DR) policy associated with the group of volumes 1108.As part of the DR policy, volumes 1108 may be replicated to the secondstorage array 1104 over the storage fabric 1106. The storage policy mayidentify those volumes in a group be replicated synchronously,replicated non-synchronously, or have a snapshot based asynchronousreplication. The storage policy may further indicate a frequency atwhich the volumes are replicated. For example, a volume 1108 in the goldgroup 1110 of volumes may be replicated more often that a volume in thesilver group 1110.

In addition to having different storage policies, the groups of volumesmay have different requirements for replication performance. Thereplication may depend on the quality of service for data frames passingthrough the storage fabric 1106. Conventionally, all replication trafficbetween the first storage array 1102 and the second storage array 1108may be treated equally by the storage fabric 1106. However, the firststorage array 1108 can assign a relative priority to each group ofvolumes to allow the storage fabric 1106 to treat different groups ofvolumes differently. For example, a volume in the bronze group 1114 maybe a assigned a relatively low priority as compared to a volume in thegold group 1110. This relative priority may be encoded by the firststorage array 1108 as described previously. Thus, when a data frameassociated with the gold volume group 1110 and a data frame associatedwith the bronze volume group 1114 are both sent to the second storagearray 1104, the hardware in the storage fabric 1106 may give the dataframe associated with the gold group 1110 priority compared to the dataframe associated with the bronze group 1114.

The hardware in the storage fabric 1106, such as Fibre Channel switchesor other interconnects in the storage fabric 1016 can treat the dataframes differently based on the priority encoded in the data frameheader. For example, available bandwidth can be divided based on theweights associated with priority values. Or in another example, the sizeof a queue may be adjusted based on a priority value. A lower prioritydata frame may have a larger associated queue and therefore greaterlatency than a higher priority data frame.

For further explanation, FIG. 12 sets forth a flow chart illustrating anexample method of preserving identities and policies across replicationaccording to embodiments of the present disclosure. The method of FIG.12 includes receiving 1210 a first data frame 1206 comprising dataassociated with a dataset stored at a first storage array 1200 and anidentifier 1208 of a quality of service level associated with the firstdata frame 1206. Receiving 1210 the first data frame 1206 may be carriedout by the first storage array 1200 receiving the first data frame 1206over a storage network fabric as part of a storage operation of a VM1202 executing on a host system. The host system may generate the dataframe 1206 to include a data payload and an identifier 1206 of a qualityof service level. The identifier 1206 of the quality of service levelmay be contained in a header of the data frame 1206 as describedpreviously. The identifier 1206 of the quality of service level mayinclude a value indicating that the data frame 1206 is carrying virtualmachine traffic, a value identifying a group of virtual machines, and/ora value identifying a particular virtual machine. In some examples, theidentifier 1206 of the quality of service level may further comprise arelative priority as explained previously.

The method of FIG. 12 also includes generating 1210 a second data frame1214 comprising a portion of the dataset and the identifier 1208 of thequality of service level associated with the first data frame 1206.Generating 1210 a second data frame 1214 may be carried out by the firststorage array 1200 generating a data frame to send to a second storagearray 1218. For example, as part of a replication process the firststorage array 1200 may generate the second data frame 1214 to send tothe second storage array 1218 to replicate the dataset 1204 on thesecond storage array 1218. The first storage array 1200 may embed theidentifier of the level of a quality of service received in the firstdata frame 1206 in the second data frame 1214. Thus, the storage fabricmay treat the second data frame 1214 with the same quality of service asthe first data frame 1206.

The method of FIG. 12 also includes transmitting 1216 the second dataframe 1214 to a second storage array 1218. Transmitting 1216 a seconddata frame 1214 may be carried out by the first storage array 1200communicating with the second storage array 1218 via the storage fabricto send the second data frame 1214 to the second storage array 1218 asdescribed earlier. The storage fabric may prioritize traffic based onthe identifier of a quality of service level contained in the seconddata frame 1214.

For further explanation, the method of FIG. 13 sets forth a flow chartillustrating another example method of preserving identities andpolicies across replication according to embodiments of the presentdisclosure. The method of FIG. 13 is similar to the method of FIG. 12 inthat the method of FIG. 13 also includes: receiving, at a first storagearray, a first data frame comprising data associated a dataset stored atthe first storage array and an identifier of a quality of service levelassociated with the first data frame; generating, at the first storagearray, a second data frame comprising a portion of the dataset and theidentifier of the quality of service level associated with the firstdata frame; and transmitting the second data frame to a second storagearray.

The method of FIG. 13 differs from the method of FIG. 12 in that, themethod of FIG. 13 also includes generating 1302, at the first storagearray 1200, a third data 1304 frame comprising data related to thedataset and an identifier of a new quality of service level 1306 havinga level higher than the quality of service level associated with thefirst data frame 1206. Generating a third data frame 1302 may be carriedout by the first storage array 1200 generating a third data frame 1304to transmit to the second storage array 1218. In contrast to the seconddata frame 1214, the third data frame 1304 may comprise data other thandata contained in the dataset. For example, the third data frame 1304may be a heartbeat message or other message that may require a higherpriority than the data in the second data frame 1214. The first storagearray 1200 may embed a priority value that is higher than a priorityvalue received in the first data frame 1206. Thus, the third data frame1304 will have a higher priority than the second data frame 1214 whentransmitted over the storage fabric.

The method of FIG. 13 also differs from the method of FIG. 12 in thatthe method of FIG. 13 also includes transmitting 1308 the third dataframe 1304 to the second storage array 1218. Transmitting 1308 the thirddata frame 1304 may be carried out by the first storage array 1200communicating with the second storage array 1218 via the storage fabricto send the third data frame 1304 to the second storage array 1218 asdescribed earlier. The storage fabric may prioritize traffic based onthe identifier of a quality of service level contained in the seconddata frame 1214 and the third data frame 1304 to prioritize the thirddata frame 1304 over the second data frame 1214.

For further explanation, the method of FIG. 14 sets forth a flow chartillustrating another example method of preserving identities andpolicies across replication according to embodiments of the presentdisclosure. The method of FIG. 14 is similar to the method of FIG. 12 inthat the method of FIG. 14 also includes: receiving 1220, at a firststorage array 1200, a first data frame 1206 comprising data associated adataset stored at the first storage array and an identifier 1208 of aquality of service level associated with the first data frame 1206;generating 1212, at the first storage array 1200, a second data frame1214 comprising a portion of the dataset and the identifier 1204 of thequality of service level associated with the first data frame 1206; andtransmitting the second data frame 1216 to a second storage array 1218.

The method of FIG. 14 differs from the method of FIG. 12 in that, themethod of FIG. 14 also includes storing 1402 the identifier 1208 of thequality of service level to the first storage array 1402. Storing theidentifier of the quality of service level may be performed by the firststorage array 1200 reading the identifier 1208 of a quality of servicelevel in the first storage frame 1206 and storing the identifier in adata store. The data store may be separate from the volume, or may bestored as part of the volume, for instance in metadata associated withthe volume. In other examples, the first storage system 1200 may have adatastore containing the identifier of the quality of service level foreach of the datasets stored at the first storage array 1200. The savedquality of service level may then be used by the first storage array1200 to generate subsequent data frames for replication between thefirst and second storage arrays 1200, 1218.

For further explanation, FIG. 15 sets forth a flow chart illustrating anexample method of preserving identities and policies across replicationaccording to embodiments of the present disclosure. The method of FIG.15 includes identifying 1502 a plurality of groups 1512, 1514 of volumes1502, 1504, 1506, 1508, wherein each group 1512, 1514 of the pluralitycomprises volumes having a common storage policy with other volumes ofthe respective group. Identifying 1502 a plurality of groups 1512, 1514of volumes 1502, 1504, 1506, 1508 may be carried out by comparingstorage policies of the volumes 1502, 1504, 1506, 1508 and groupingvolumes having storage policies that have at least one commoncharacteristic. For examples, volumes 1502, 1504, 1506, 1508 may begrouped according to matches in a storage policy such as commonreplication interval, a common priority, a common tenant accessing thevolume, or some other storage policy requirement. In FIG. 15, the firstvolume 1502 and the second volume 1504 are grouped together as a firstgroup 1512 and the third volume 1506 and the fourth volume 1508 aregrouped together as a second group 1514. One of skill in the art willrecognize that a group may have more than two volumes and that more thantwo groups are possible.

The method of FIG. 15 also includes encoding 1516 a first identifier1524 of a first quality of service level corresponding to a firststorage policy for a first group 1512 in a first data frame 1520. Thismay be accomplished by assigning a priority to a group based on thestorage policy in common for that group. In some examples, the storagepolicy may specify a priority level for the volumes in the group.However, in other examples the storage policy may be translated into arelative priority level. For example, if the first volume group 1512 hasa higher guaranteed service level than the second volume group 1514,then the first volume group 1512 may have a higher identifier of astorage service level than the second volume group 1514. Or in anotherexample, if there are three storage volume groups each having differentservice level agreements, the service level agreements may be comparedto determine which volume group should have the highest priority.

The method of FIG. 15 also includes encoding 1518 a second identifier1526 of a second quality of service level corresponding to a secondstorage policy for the second group 1514 in a second data frame 1518,wherein the first quality of service level is higher than the secondpriority of service level. Encoding 1518 a second identifier 1526 of asecond quality of service may be performed in the same manner asencoding a first identifier 1524 of a first quality of service level,with the exception that the encoding is performed on a second data frame1522. The second identifier 1522 may be encoded to have a lower prioritythan the first identifier 1524 indicating that the first data frame 1520should be treated with a higher priority than the second data frame1522.

The method of FIG. 15 also includes transmitting the first data frame1520 and the second data frame 1522 between a first storage array 1500and a second storage array 1530, wherein the first data frame 1520 issent with a higher priority than the second data frame 1522. This may beaccomplished by the first storage array 1500 communicating with thesecond storage array 1530 over a storage fabric. Once the first dataframe 1520 and the second data frame 1522 leave the first storage array1500, the storage fabric routes the data frames 1520, 1522 to the secondstorage array 1530 while prioritizing the first data frame 1520. Forexample, the storage fabric may implement a smaller storage buffer fortransmitting the first data frame 1520 and/or the storage fabric mayrelay the first data frame 1520 prior to relaying the second data frame1522. Or in other examples, the available bandwidth may be allocated totransmission of the data frames 1520, 1522 based on their relativepriorities.

Readers will appreciate that although the examples described above aredepicted in a way where steps occur in a certain order, no such orderingof steps is required unless explicitly stated. Example embodiments aredescribed largely in the context of a fully functional computer system.Readers of skill in the art will recognize, however, that the presentdisclosure also may be embodied in a computer program product disposedupon computer readable storage media for use with any suitable dataprocessing system. Such computer readable storage media may be anystorage medium for machine-readable information, including magneticmedia, optical media, or other suitable media. Examples of such mediainclude magnetic disks in hard drives or diskettes, compact disks foroptical drives, magnetic tape, and others as will occur to those ofskill in the art. Persons skilled in the art will immediately recognizethat any computer system having suitable programming means will becapable of executing the steps of the method as embodied in a computerprogram product. Persons skilled in the art will recognize also that,although some of the example embodiments described in this specificationare oriented to software installed and executing on computer hardware,nevertheless, alternative embodiments implemented as firmware or ashardware are well within the scope of the present disclosure.

Embodiments can include be a system, a method, and/or a computer programproduct. The computer program product may include a computer readablestorage medium (or media) having computer readable program instructionsthereon for causing a processor to carry out aspects of the presentdisclosure.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present disclosure may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present disclosure.

Aspects of the present disclosure are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to some embodimentsof the disclosure. It will be understood that each block of theflowchart illustrations and/or block diagrams, and combinations ofblocks in the flowchart illustrations and/or block diagrams, can beimplemented by computer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present disclosure. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

What is claimed is:
 1. A method comprising: receiving, at a firststorage array, a first data frame comprising data associated a datasetstored at the first storage array and an identifier of a quality ofservice level associated with the first data frame; generating, at thefirst storage array, a second data frame comprising a portion of thedataset and the identifier of the quality of service level associatedwith the first data frame; and transmitting the second data frame to asecond storage array.
 2. The method of claim 1, wherein the identifierof a quality of service level comprises an identifier of a prioritylevel for a group of storage tenants having a common storage policy. 3.The method of claim 1, wherein the identifier of a quality of servicelevel comprises an identification of traffic originating from a virtualmachine.
 4. The method of claim 1, further comprising: generating, atthe first storage array, a third data frame comprising data related tothe dataset and the identifier of a new quality of service level higherthan the quality of service level associated with the first data frame;and transmitting the third data frame to the second storage array. 5.The method of claim 1, wherein the first data frame is received over afirst network and the second data frame is transmitted over a secondnetwork, wherein the first network and the second network are differentnetworks.
 6. The method of claim 1, wherein the second data frame isgenerated and transmitted to the second storage array as part of areplication process.
 7. The method of claim 1, further comprisingstoring the identifier of the quality of service level to the firststorage array.
 8. An apparatus including a computer processor and acomputer memory operatively coupled to the computer processor, thecomputer memory having disposed within it computer program instructionsthat, when executed by the computer processor, cause the apparatus tocarry out the steps of: receiving, at a first storage array, a firstdata frame comprising data associated a dataset stored at the firststorage array and an identifier of a quality of service level associatedwith the first data frame; generating, at the first storage array, asecond data frame comprising a portion of the dataset and the identifierof the quality of service level associated with the first data frame;and transmitting the second data frame to a second storage array.
 9. Theapparatus of claim 8, wherein the identifier of a quality of servicelevel comprises an identifier of a priority level for a group of storagetenants having a common storage policy.
 10. The apparatus of claim 8,wherein the identifier of a quality of service level comprises anidentification of traffic originating from a virtual machine.
 11. Theapparatus of claim 8, further comprising computer program instructionsthat, when executed by the computer processor, cause the apparatus tocarry out the steps of: generating, at the first storage array, a thirddata frame comprising a data related to the dataset and the identifierof a new quality of service level higher than the quality of servicelevel associated with the first data frame; and transmitting the seconddata frame to a second storage array.
 12. The apparatus of claim 8,wherein the first data frame is received over a first network and thesecond data frame is transmitted over a second network, wherein thefirst network and the second network are different networks.
 13. Theapparatus of claim 8, wherein the second data frame is generated andtransmitted to the second storage array as part of a replicationprocess.
 14. The apparatus of claim 8, further comprising computerprogram instructions that, when executed by the computer processor,cause the apparatus to carry out the step of storing the identifier ofthe quality of service level to the first storage array.
 15. A methodcomprising: identifying a plurality of groups of volumes, wherein eachgroup of the plurality comprises volumes having a common storage policywith other volumes of the respective group; encoding a first identifierof a first quality of service level corresponding to a first storagepolicy for a first group among the plurality of groups in a first dataframe; encoding a second identifier of a second quality of service levelcorresponding to a second storage policy for a second group among theplurality of groups in a second data frame, wherein the first quality ofservice level is higher than the second quality of service level; andtransmitting the first data frame and the second data frame between afirst storage array and a second storage array, wherein the first dataframe is sent with a higher priority than the second data frame.
 16. Themethod of claim 15 wherein the first data frame and the second dataframe are sent as part of a replication process.
 17. The method of claim16 wherein the replication process is synchronous replication.
 18. Themethod of claim 15 wherein the first identifier of a first quality ofservice level comprises an identifier of a priority level for a group ofstorage tenants.
 19. The method of claim 15 wherein the first identifierof a first quality of service level comprises an identification oftraffic originating from a virtual machine.
 20. The method of claim 15wherein the first identifier of a first quality of service level isincluded in a CS_CTL of the first frame.